[Tutorialsplanet.NET] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

Creation Time	Nov. 25, 2024, 12:36 p.m.
Last Access Time	March 9, 2025, 1:44 a.m.
File Size	15.3 GB
Keywords	Tutorialsplanet Complete Science - Udemy Course 2020 NET Bootcamp Data The
Total Requests	26
Total Files	1513
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 160.5 MB
12. Probability - Distributions/29. A Practical Example of Probability Distributions.mp4 157.8 MB
11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4 145.1 MB
40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4 144.3 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 138.3 MB
10. Probability - Combinatorics/20. A Practical Example of Combinatorics.mp4 134.3 MB
3/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 126.9 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125.2 MB
56. Software Integration/5. Taking a Closer Look at APIs.mp4 115.6 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 111.7 MB
2/7. Continuing with BI, ML, and AI.mp4 109.0 MB
56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 104.1 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 103.5 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4 103.4 MB
19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 102.7 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99.3 MB
13. Probability - Probability in Other Fields/1. Probability in Finance.mp4 99.1 MB
35/1. Practical Example Linear Regression (Part 1).mp4 97.1 MB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 92.1 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 90.0 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting Acquainted with the Dataset.mp4 87.7 MB
36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 86.5 MB
9. Part 2 Probability/1. The Basic Probability Formula.mp4 85.9 MB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp4 84.3 MB
12. Probability - Distributions/15. Characteristics of Continuous Distributions.mp4 84.1 MB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 82.6 MB
2/1. Data Science and Business Buzzwords Why are there so Many.mp4 81.4 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason Behind These Disciplines.mp4 81.2 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 81.1 MB
18/3. Confidence Intervals; Population Variance Known; Z-score.mp4 78.2 MB
13. Probability - Probability in Other Fields/2. Probability in Statistics.mp4 77.3 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp4 76.4 MB
9. Part 2 Probability/3. Computing Expected Values.mp4 75.7 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 75.5 MB
22. Part 4 Introduction to Python/3. Why Python.mp4 75.1 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 74.6 MB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 74.5 MB
12. Probability - Distributions/1. Fundamentals of Probability Distributions.mp4 73.4 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 72.9 MB
15. Statistics - Descriptive Statistics/1. Types of Data.mp4 72.5 MB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 71.5 MB
12. Probability - Distributions/3. Types of Probability Distributions.mp4 71.1 MB
18/12. Confidence intervals. Two means. Dependent samples.mp4 70.5 MB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 69.5 MB
56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 69.0 MB
12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.mp4 68.8 MB
2/9. A Breakdown of our Data Science Infographic.mp4 67.7 MB
51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp4 66.3 MB
2/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64.5 MB
56. Software Integration/9. Software Integration - Explained.mp4 63.7 MB
13. Probability - Probability in Other Fields/3. Probability in Data Science.mp4 63.5 MB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 62.9 MB
54/9. MNIST Results and Testing.mp4 62.8 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4 62.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 61.9 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 61.8 MB
9. Part 2 Probability/5. Frequency.mp4 61.7 MB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 61.6 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61.1 MB
56. Software Integration/7. Communication between Software Products through Text Files.mp4 60.3 MB
45/3. Digging into a Deep Net.mp4 59.4 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 59.3 MB
9. Part 2 Probability/7. Events and Their Complements.mp4 59.2 MB
52. Deep Learning - Conclusion/4. An overview of CNNs.mp4 58.8 MB
22. Part 4 Introduction to Python/1. Introduction to Programming.mp4 58.5 MB
14. Part 3 Statistics/1. Population and Sample.mp4 58.1 MB
35/8. Practical Example Linear Regression (Part 5).mp4 57.9 MB
32/1. The Linear Regression Model.mp4 57.4 MB
10. Probability - Combinatorics/11. Solving Combinations.mp4 57.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 57.3 MB
11. Probability - Bayesian Inference/7. Union of Sets.mp4 57.2 MB
18/5. Confidence Interval Clarifications.mp4 57.0 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 56.6 MB
54/4. MNIST Model Outline.mp4 56.4 MB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 56.1 MB
35/6. Practical Example Linear Regression (Part 4).mp4 56.1 MB
20. Statistics - Hypothesis Testing/10. p-value.mp4 55.9 MB
12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.mp4 55.8 MB
33/18. Dealing with Categorical Data - Dummy Variables.mp4 55.7 MB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 55.6 MB
62. Appendix - Additional Python Tools/5. List Comprehensions.mp4 55.5 MB
33/3. Adjusted R-Squared.mp4 54.8 MB
15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54.4 MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 54.4 MB
60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 54.3 MB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54.2 MB
2/3. What is the difference between Analysis and Analytics.mp4 53.6 MB
11. Probability - Bayesian Inference/1. Sets and Events.mp4 53.5 MB
37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53.4 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp4 53.1 MB
59/5. Splitting the Data for Training and Testing.mp4 52.8 MB
59/8. Interpreting the Coefficients for Our Problem.mp4 52.4 MB
57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 52.3 MB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 51.8 MB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4 51.0 MB
49. Deep Learning - Preprocessing/3. Standardization.mp4 51.0 MB
15. Statistics - Descriptive Statistics/22. Variance.mp4 51.0 MB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50.4 MB
18/1. What are Confidence Intervals.mp4 50.0 MB
11. Probability - Bayesian Inference/20. Bayes' Law.mp4 49.9 MB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 49.9 MB
51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp4 49.8 MB
40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4 49.8 MB
32/13. Decomposition of Variability.mp4 49.7 MB
40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 49.4 MB
34/19. Train - Test Split Explained.mp4 49.2 MB
59/12. Testing the Model We Created.mp4 49.1 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 49.0 MB
11. Probability - Bayesian Inference/18. The Multiplication Law.mp4 49.0 MB
12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.mp4 48.2 MB
12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4 47.9 MB
17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 47.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 47.8 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp4 47.7 MB
62. Appendix - Additional Python Tools/1. Using the .format() Method.mp4 47.6 MB
11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp4 47.4 MB
18/10. Margin of Error.mp4 47.2 MB
12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp4 47.1 MB
54/8. MNIST Learning.mp4 46.7 MB
62. Appendix - Additional Python Tools/4. Triple Nested For Loops.mp4 46.6 MB
35/2. Practical Example Linear Regression (Part 2).mp4 46.0 MB
11. Probability - Bayesian Inference/13. The Conditional Probability Formula.mp4 45.9 MB
59/2. Creating the Targets for the Logistic Regression.mp4 45.8 MB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 45.1 MB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45.1 MB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 44.8 MB
32/11. How to Interpret the Regression Table.mp4 44.6 MB
39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 44.6 MB
32/8. First Regression in Python.mp4 44.6 MB
59/16. Preparing the Deployment of the Model through a Module.mp4 44.5 MB
22. Part 4 Introduction to Python/5. Why Jupyter.mp4 44.3 MB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 44.1 MB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 43.9 MB
54/6. Calculating the Accuracy of the Model.mp4 43.9 MB
10. Probability - Combinatorics/9. Solving Variations without Repetition.mp4 43.1 MB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 43.0 MB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 42.9 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 42.8 MB
10. Probability - Combinatorics/3. Permutations and How to Use Them.mp4 42.7 MB
33/13. A3 Normality and Homoscedasticity.mp4 42.7 MB
28. Python - Sequences/7. Dictionaries.mp4 41.7 MB
59/6. Fitting the Model and Assessing its Accuracy.mp4 41.6 MB
50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp4 41.5 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp4 41.5 MB
10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 41.3 MB
59/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 41.2 MB
32/17. R-Squared.mp4 41.0 MB
50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4 41.0 MB
57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 40.9 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 40.6 MB
32/7. Python Packages Installation.mp4 40.6 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 40.6 MB
59/10. Interpreting the Coefficients of the Logistic Regression.mp4 40.4 MB
10. Probability - Combinatorics/13. Symmetry of Combinations.mp4 40.3 MB
12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp4 40.2 MB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40.2 MB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 39.8 MB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 39.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 39.6 MB
59/11. Backward Elimination or How to Simplify Your Model.mp4 39.6 MB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39.4 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp4 39.4 MB
57. Case Study - What's Next in the Course/2. The Business Task.mp4 39.2 MB
34/14. Feature Scaling (Standardization).mp4 39.1 MB
59/7. Creating a Summary Table with the Coefficients and Intercept.mp4 38.9 MB
44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp4 38.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 38.7 MB
62. Appendix - Additional Python Tools/6. Anonymous (Lambda) Functions.mp4 38.5 MB
10. Probability - Combinatorics/19. A Recap of Combinatorics.mp4 38.5 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38.5 MB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 38.4 MB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38.3 MB
40. Part 6 Mathematics/13. Transpose of a Matrix.mp4 38.1 MB
28. Python - Sequences/1. Lists.mp4 37.8 MB
38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 37.7 MB
28. Python - Sequences/3. Using Methods.mp4 37.6 MB
59/13. Saving the Model and Preparing it for Deployment.mp4 37.5 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp4 37.4 MB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37.2 MB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 37.1 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 36.8 MB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 36.6 MB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent Samples (Part 2).mp4 36.4 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4 36.4 MB
37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36.2 MB
10. Probability - Combinatorics/5. Simple Operations with Factorials.mp4 36.1 MB
33/11. A2 No Endogeneity.mp4 35.7 MB
18/6. Student's T Distribution.mp4 35.4 MB
45/7. Backpropagation.mp4 35.0 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 34.9 MB
11. Probability - Bayesian Inference/15. The Law of Total Probability.mp4 34.9 MB
34/15. Feature Selection through Standardization of Weights.mp4 34.9 MB
11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp4 34.8 MB
34/3. Simple Linear Regression with sklearn.mp4 34.8 MB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 34.7 MB
44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.mp4 34.7 MB
12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4 34.1 MB
10. Probability - Combinatorics/7. Solving Variations with Repetition.mp4 34.0 MB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent Samples (Part 1).mp4 33.9 MB
40. Part 6 Mathematics/3. Scalars and Vectors.mp4 33.8 MB
30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 33.6 MB
40. Part 6 Mathematics/1. What is a Matrix.mp4 33.6 MB
44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.mp4 33.5 MB
10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 33.1 MB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 32.9 MB
46. Deep Learning - Overfitting/3. What is Validation.mp4 32.7 MB
40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4 32.6 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32.5 MB
36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 32.3 MB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 32.3 MB
18/8. Confidence Intervals; Population Variance Unknown; T-score.mp4 32.2 MB
34/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 32.0 MB
33/14. A4 No Autocorrelation.mp4 31.5 MB
51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp4 31.2 MB
41. Part 7 Deep Learning/1. What to Expect from this Part.mp4 31.1 MB
46. Deep Learning - Overfitting/1. What is Overfitting.mp4 31.1 MB
34/8. Calculating the Adjusted R-Squared in sklearn.mp4 30.9 MB
28. Python - Sequences/5. List Slicing.mp4 30.8 MB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 30.6 MB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 30.6 MB
51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp4 30.4 MB
44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.mp4 30.3 MB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 30.1 MB
25. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30.1 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 29.9 MB
29. Python - Iterations/8. How to Iterate over Dictionaries.mp4 29.7 MB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 29.6 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 29.5 MB
45/2. What is a Deep Net.mp4 29.5 MB
34/10. Feature Selection (F-regression).mp4 29.5 MB
50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4 29.5 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 29.5 MB
28. Python - Sequences/6. Tuples.mp4 29.5 MB
62. Appendix - Additional Python Tools/3. Introduction to Nested For Loops.mp4 29.5 MB
15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 29.4 MB
48/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 29.1 MB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29.1 MB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 29.1 MB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 29.0 MB
18/14. Confidence intervals. Two means. Independent Samples (Part 1).mp4 28.8 MB
33/16. A5 No Multicollinearity.mp4 28.7 MB
42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 28.7 MB
48/1. Stochastic Gradient Descent.mp4 28.7 MB
42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28.4 MB
29. Python - Iterations/3. While Loops and Incrementing.mp4 28.4 MB
32/15. What is the OLS.mp4 28.3 MB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp4 28.2 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 28.0 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 27.9 MB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 27.8 MB
29. Python - Iterations/6. Conditional Statements and Loops.mp4 27.8 MB
45/4. Non-Linearities and their Purpose.mp4 27.7 MB
59/1. Exploring the Problem with a Machine Learning Mindset.mp4 27.5 MB
15. Statistics - Descriptive Statistics/27. Covariance.mp4 27.5 MB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27.3 MB
34/1. What is sklearn and How is it Different from Other Packages.mp4 27.3 MB
12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.mp4 27.2 MB
36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27.1 MB
11. Probability - Bayesian Inference/16. The Additive Rule.mp4 27.0 MB
11. Probability - Bayesian Inference/5. Intersection of Sets.mp4 27.0 MB
18/16. Confidence intervals. Two means. Independent Samples (Part 2).mp4 26.8 MB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 26.7 MB
48/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 26.4 MB
12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4 26.3 MB
34/16. Predicting with the Standardized Coefficients.mp4 26.0 MB
45/6. Activation Functions Softmax Activation.mp4 25.9 MB
54/5. MNIST Loss and Optimization Algorithm.mp4 25.9 MB
15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 25.9 MB
29. Python - Iterations/4. Lists with the range() Function.mp4 25.8 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp4 25.7 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 25.7 MB
60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 25.5 MB
11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.mp4 25.4 MB
52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25.3 MB
46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 25.2 MB
42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 25.1 MB
45/5. Activation Functions.mp4 25.1 MB
46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 25.1 MB
26. Python - Conditional Statements/4. The ELIF Statement.mp4 25.1 MB
33/20. Making Predictions with the Linear Regression.mp4 24.7 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 24.4 MB
12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.mp4 24.4 MB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 24.2 MB
23. Python - Variables and Data Types/5. Python Strings.mp4 24.2 MB
40. Part 6 Mathematics/14. Dot Product.mp4 24.0 MB
35/4. Practical Example Linear Regression (Part 3).mp4 23.7 MB
29. Python - Iterations/1. For Loops.mp4 23.6 MB
42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4 23.2 MB
36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 23.1 MB
44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.mp4 22.9 MB
17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4 22.8 MB
12. Probability - Distributions/5. Characteristics of Discrete Distributions.mp4 22.7 MB
42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 22.7 MB
54/2. MNIST How to Tackle the MNIST.mp4 22.6 MB
40. Part 6 Mathematics/8. What is a Tensor.mp4 22.5 MB
17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 22.5 MB
62. Appendix - Additional Python Tools/2. Iterating Over Range Objects.mp4 22.5 MB
48/7. Adam (Adaptive Moment Estimation).mp4 22.4 MB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 22.3 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 22.0 MB
27. Python - Python Functions/7. Built-in Functions in Python.mp4 22.0 MB
44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.mp4 22.0 MB
33/7. OLS Assumptions.mp4 21.9 MB
47. Deep Learning - Initialization/1. What is Initialization.mp4 21.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 21.6 MB
33/1. Multiple Linear Regression.mp4 21.5 MB
38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4 21.2 MB
46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4 20.7 MB
59/4. Standardizing the Data.mp4 20.6 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4 20.6 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.mp4 20.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp4 20.2 MB
52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 20.1 MB
34/7. Multiple Linear Regression with sklearn.mp4 20.1 MB
18/18. Confidence intervals. Two means. Independent Samples (Part 3).mp4 19.9 MB
30. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 19.9 MB
45/8. Backpropagation Picture.mp4 19.5 MB
34/2. How are we Going to Approach this Section.mp4 19.4 MB
15. Statistics - Descriptive Statistics/19. Skewness.mp4 19.4 MB
24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 18.9 MB
54/3. MNIST Relevant Packages.mp4 18.9 MB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 18.7 MB
49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4 18.6 MB
27. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 18.1 MB
30. Python - Advanced Python Tools/5. What is the Standard Library.mp4 18.0 MB
42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 17.9 MB
54/1. MNIST What is the MNIST Dataset.mp4 17.8 MB
51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.mp4 17.6 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.mp4 17.4 MB
31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 17.3 MB
47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 17.1 MB
36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17.1 MB
23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17.1 MB
34/18. Underfitting and Overfitting.mp4 16.9 MB
59/3. Selecting the Inputs for the Logistic Regression.mp4 16.8 MB
48/3. Momentum.mp4 16.4 MB
33/6. Test for Significance of the Model (F-Test).mp4 16.4 MB
44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.mp4 16.4 MB
50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.mp4 16.3 MB
10. Probability - Combinatorics/1. Fundamentals of Combinatorics.mp4 16.2 MB
27. Python - Python Functions/5. Conditional Statements and Functions.mp4 15.7 MB
17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 15.5 MB
32/3. Correlation vs Regression.mp4 14.7 MB
37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 14.6 MB
47. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 14.3 MB
23. Python - Variables and Data Types/1. Variables.mp4 14.1 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4 14.0 MB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.mp4 13.9 MB
22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 13.8 MB
15. Statistics - Descriptive Statistics/11. The Histogram.mp4 13.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4 13.7 MB
50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.mp4 13.4 MB
54/7. MNIST Batching and Early Stopping.mp4 12.9 MB
33/9. A1 Linearity.mp4 12.6 MB
45/1. What is a Layer.mp4 12.5 MB
34/12. Creating a Summary Table with P-values.mp4 12.3 MB
32/10. Using Seaborn for Graphs.mp4 12.2 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.mp4 12.2 MB
49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 11.9 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.mp4 11.4 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp4 11.2 MB
40. Part 6 Mathematics/12. Errors when Adding Matrices.mp4 11.2 MB
27. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 11.1 MB
48/2. Problems with Gradient Descent.mp4 11.0 MB
26. Python - Conditional Statements/3. The ELSE Statement.mp4 10.8 MB
26. Python - Conditional Statements/1. The IF Statement.mp4 10.8 MB
51. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp4 10.8 MB
25. Python - Other Python Operators/1. Comparison Operators.mp4 10.2 MB
38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4 9.9 MB
29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4 9.5 MB
48/5. Learning Rate Schedules Visualized.mp4 9.1 MB
26. Python - Conditional Statements/5. A Note on Boolean Values.mp4 8.9 MB
12. Probability - Distributions/29.3 FIFA19 (post).csv 8.6 MB
12. Probability - Distributions/29.4 FIFA19.csv 8.6 MB
30. Python - Advanced Python Tools/3. Modules and Packages.mp4 8.5 MB
27. Python - Python Functions/4. How to Use a Function within a Function.mp4 8.1 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/29.3 Absenteeism Exercise - Preprocessing LECTURES.ipynb 7.6 MB
51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 7.3 MB
2/7.2 365_DataScience.png 6.9 MB
2/9.1 365_DataScience.png 6.9 MB
44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.mp4 6.8 MB
27. Python - Python Functions/1. Defining a Function in Python.mp4 6.3 MB
27. Python - Python Functions/6. Functions Containing a Few Arguments.mp4 6.0 MB
24. Python - Basic Python Syntax/3. The Double Equality Sign.mp4 6.0 MB
24. Python - Basic Python Syntax/10. Indexing Elements.mp4 5.9 MB
24. Python - Basic Python Syntax/12. Structuring with Indentation.mp4 5.5 MB
32/5. Geometrical Representation of the Linear Regression Model.mp4 5.1 MB
24. Python - Basic Python Syntax/7. Add Comments.mp4 4.7 MB
24. Python - Basic Python Syntax/5. How to Reassign Values.mp4 4.0 MB
24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4 2.4 MB
23. Python - Variables and Data Types/1.1 Python Introduction - Course Notes.pdf 2.0 MB
19. Statistics - Practical Example Inferential Statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.8 MB
19. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx 1.7 MB
19. Statistics - Practical Example Inferential Statistics/2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.7 MB
20. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf 1.2 MB
45/1.1 Course Notes - Section 6.pdf 936.4 KB
45/2.1 Course Notes - Section 6.pdf 936.4 KB
11. Probability - Bayesian Inference/22.2 CDS_2017-2018 Hamilton.pdf 845.3 KB
35/8.3 sklearn - Linear Regression - Practical Example (Part 5)_with_comments.ipynb 711.0 KB
51. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1.1 Audiobooks_data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11.1 Audiobooks_data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/12.1 Audiobooks_data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3.1 Audiobooks-data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4.3 Audiobooks_data.csv 710.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/5.3 Audiobooks_data.csv 710.8 KB
35/8.1 sklearn - Linear Regression - Practical Example (Part 5).ipynb 698.4 KB
20. Statistics - Hypothesis Testing/1.1 Course notes_hypothesis_testing.pdf 656.4 KB
20. Statistics - Hypothesis Testing/4.1 Course notes_hypothesis_testing.pdf 656.4 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.1 Shortcuts-for-Jupyter.pdf 619.2 KB
44. Deep Learning - TensorFlow 2.0 Introduction/1.1 Shortcuts-for-Jupyter.pdf 619.2 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5.1 Shortcuts-for-Jupyter.pdf 619.2 KB
42. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf 588.1 KB
42. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf 578.1 KB
14. Part 3 Statistics/1.1 Course notes_descriptive_statistics.pdf 482.2 KB
15. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf 482.2 KB
12. Probability - Distributions/1.1 Course Notes - Probability Distributions.pdf 463.9 KB
35/6.3 sklearn - Linear Regression - Practical Example (Part 4)_with_comments.ipynb 407.6 KB
35/6.1 sklearn - Linear Regression - Practical Example (Part 4).ipynb 397.2 KB
11. Probability - Bayesian Inference/1.1 Course Notes - Bayesian Inference.pdf 386.0 KB
17. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf 382.3 KB
17. Statistics - Inferential Statistics Fundamentals/2.1 Course notes_inferential statistics.pdf 382.3 KB
9. Part 2 Probability/1.1 Course Notes - Basic Probability.pdf 371.1 KB
35/5.1 sklearn - Dummies and VIF - Exercise Solution.ipynb 370.2 KB
35/4.2 sklearn - Linear Regression - Practical Example (Part 3)_with_comments.ipynb 351.5 KB
35/5.3 sklearn - Dummies and VIF - Exercise.ipynb 344.6 KB
12. Probability - Distributions/15.1 Solving Integrals.pdf 343.9 KB
35/4.1 sklearn - Linear Regression - Practical Example (Part 3).ipynb 343.6 KB
35/2.1 sklearn - Linear Regression - Practical Example (Part 2)_with_comments.ipynb 335.6 KB
36. Advanced Statistical Methods - Logistic Regression/1.1 Course_Notes_Logistic_Regression.pdf 335.2 KB
36. Advanced Statistical Methods - Logistic Regression/2.4 Course_Notes_Logistic_Regression.pdf 335.2 KB
35/2.2 sklearn - Linear Regression - Practical Example (Part 2).ipynb 328.7 KB
2/5.1 365_DataScience_Diagram.pdf 323.1 KB
2/7.1 365_DataScience_Diagram.pdf 323.1 KB
13. Probability - Probability in Other Fields/3.1 Probability Cheat Sheet.pdf 320.3 KB
31. Part 5 Advanced Statistical Methods in Python/1.1 Course notes_regression_analysis.pdf 312.2 KB
32/1.1 Course notes_regression_analysis.pdf 312.2 KB
1. Part 1 Introduction/3.2 FAQ_The_Data_Science_Course.pdf 306.1 KB
15. Statistics - Descriptive Statistics/13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.1 KB
15. Statistics - Descriptive Statistics/7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.1 KB
10. Probability - Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf 245.7 KB
35/5.2 1.04. Real-life example.csv 229.8 KB
10. Probability - Combinatorics/1.1 Course Notes - Combinatorics.pdf 226.1 KB
35/1.3 1.04. Real-life example.csv 219.8 KB
35/2.3 1.04. Real-life example.csv 219.8 KB
35/6.2 1.04. Real-life example.csv 219.8 KB
35/8.2 1.04. Real-life example.csv 219.8 KB
37. Advanced Statistical Methods - Cluster Analysis/1.1 Course_Notes_Cluster_Analysis.pdf 208.7 KB
37. Advanced Statistical Methods - Cluster Analysis/2.1 Course_Notes_Cluster_Analysis.pdf 208.7 KB
10. Probability - Combinatorics/11.1 Combinations With Repetition.pdf 207.4 KB
13. Probability - Probability in Other Fields/1.2 Probability in Finance Solutions.pdf 184.5 KB
45/9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
35/1.1 sklearn - Linear Regression - Practical Example (Part 1)_with_comments.ipynb 171.4 KB
35/1.2 sklearn - Linear Regression - Practical Example (Part 1).ipynb 166.9 KB
16. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx 146.5 KB
16. Statistics - Practical Example Descriptive Statistics/2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146.4 KB
12. Probability - Distributions/13.1 Poisson - Expected Value and Variance.pdf 146.0 KB
12. Probability - Distributions/17.1 Normal Distribution - Exp and Var.pdf 144.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.1 data_preprocessing_homework.pdf 134.5 KB
16. Statistics - Practical Example Descriptive Statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.3 KB
36. Advanced Statistical Methods - Logistic Regression/16.4 Testing the Model - Solution.ipynb 111.1 KB
13. Probability - Probability in Other Fields/1.1 Probability in Finance Homework.pdf 110.7 KB
10. Probability - Combinatorics/20.1 Additional Exercises Combinatorics.pdf 106.6 KB
10. Probability - Combinatorics/13.1 Symmetry Explained.pdf 95.0 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9.5 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb 84.4 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.8 Minimal_example_Exercise_3.d. Solution.ipynb 84.1 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9.1 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb 83.7 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9.3 TensorFlow_Minimal_example_All_exercises.ipynb 83.6 KB
44. Deep Learning - TensorFlow 2.0 Introduction/8.2 TensorFlow_Minimal_example_complete_with_comments.ipynb 82.3 KB
36. Advanced Statistical Methods - Logistic Regression/13.2 Calculating the Accuracy of the Model - Solution.ipynb 81.2 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9.2 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb 77.5 KB
44. Deep Learning - TensorFlow 2.0 Introduction/8.1 TensorFlow_Minimal_example_complete.ipynb 76.9 KB
44. Deep Learning - TensorFlow 2.0 Introduction/7.1 TensorFlow_Minimal_example_Part3.ipynb 76.5 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.11 Minimal_example_Exercise_3.c. Solution.ipynb 70.1 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.2 Minimal_example_Exercise_1_Solution.ipynb 69.0 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Minimal_example_Exercise_5_Solution.ipynb 68.9 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 Minimal_example_Exercise_3.a. Solution.ipynb 67.9 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Minimal_example_Exercise_3.b. Solution.ipynb 67.7 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Minimal_example_Exercise_4_Solution.ipynb 66.5 KB
60. Case Study - Loading the 'absenteeism_module'/1.4 Absenteeism Exercise - Integration.ipynb 62.4 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Minimal_example_Exercise_6_Solution.ipynb 61.8 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Minimal_example_Exercise_6.ipynb 61.8 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.7 Minimal_example_Exercise_2_Solution.ipynb 61.4 KB
21. Statistics - Practical Example Hypothesis Testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx 51.9 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.5 TensorFlow_Minimal_Example_Exercise_2_3_Solution.ipynb 50.0 KB
21. Statistics - Practical Example Hypothesis Testing/2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44.3 KB
21. Statistics - Practical Example Hypothesis Testing/2.2 4.10.+Hypothesis+testing+section_practical+example_exercise.xlsx 43.7 KB
42. Deep Learning - Introduction to Neural Networks/21.1 GD-function-example.xlsx 42.3 KB
15. Statistics - Descriptive Statistics/7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx 41.1 KB
15. Statistics - Descriptive Statistics/16.1 2.6. Cross table and scatter plot_exercise_solution.xlsx 40.4 KB
15. Statistics - Descriptive Statistics/19.1 2.8. Skewness_lesson.xlsx 34.6 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.2 Absenteeism_data.csv 32.0 KB
15. Statistics - Descriptive Statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 30.8 KB
11. Probability - Bayesian Inference/22.3 Bayesian Homework - Solutions.pdf 30.4 KB
34/16.2 sklearn - Making Predictions with the Standardized Coefficients.ipynb 29.8 KB
15. Statistics - Descriptive Statistics/29.1 2.11. Covariance_exercise_solution.xlsx 29.5 KB
15. Statistics - Descriptive Statistics/32.2 2.12. Correlation_exercise_solution.xlsx 29.5 KB
15. Statistics - Descriptive Statistics/32.1 2.12. Correlation_exercise.xlsx 29.3 KB
59/1.1 Absenteeism_preprocessed.csv 29.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.3 df_preprocessed.csv 29.1 KB
34/4.3 sklearn - Simple Linear Regression_with_comments.ipynb 28.4 KB
34/6.1 sklearn - Simple Linear Regression_with_comments.ipynb 28.4 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9.4 TensorFlow_Minimal_example_Exercise_1_Solution.ipynb 28.0 KB
11. Probability - Bayesian Inference/22.1 Bayesian Homework .pdf 27.3 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.2 TensorFlow_Minimal_Example_Exercise_4_Solution.ipynb 27.0 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.7 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb 26.7 KB
15. Statistics - Descriptive Statistics/14.1 2.6. Cross table and scatter plot.xlsx 26.1 KB
34/4.2 sklearn - Simple Linear Regression.ipynb 26.1 KB
34/6.2 sklearn - Simple Linear Regression.ipynb 26.1 KB
18/3.1 3.9.The-z-table.xlsx 25.6 KB
18/4.3 3.9.The-z-table.xlsx 25.6 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb 25.5 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.8 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb 25.5 KB
62. Appendix - Additional Python Tools/1.2 Additional-Python-Tools-Solutions.ipynb 25.5 KB
62. Appendix - Additional Python Tools/6.3 Additional-Python-Tools-Solutions.ipynb 25.5 KB
15. Statistics - Descriptive Statistics/27.1 2.11. Covariance_lesson.xlsx 24.9 KB
17. Statistics - Inferential Statistics Fundamentals/8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.0 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.6 TensorFlow_Minimal_Example_Exercise_1_Solution.ipynb 23.6 KB
34/16.3 sklearn - Making Predictions with the Standardized Coefficients_with_comments.ipynb 22.0 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.1 TensorFlow_Minimal_Example_Exercise_2_4_Solution.ipynb 21.7 KB
1. Part 1 Introduction/3. Download All Resources and Important FAQ.html 21.4 KB
16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.srt 20.8 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.3 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb 20.6 KB
14. Part 3 Statistics/1.2 Statistics Glossary.xlsx 20.3 KB
15. Statistics - Descriptive Statistics/29.2 2.11. Covariance_exercise.xlsx 20.2 KB
12. Probability - Distributions/29.6 Daily Views (post).xlsx 20.2 KB
15. Statistics - Descriptive Statistics/1.2 Glossary.xlsx 20.0 KB
12. Probability - Distributions/29. A Practical Example of Probability Distributions.srt 19.9 KB
15. Statistics - Descriptive Statistics/21.1 2.8. Skewness_exercise_solution.xlsx 19.8 KB
51. Deep Learning - Business Case Example/8.1 TensorFlow_Audiobooks_Machine_Learning_Part2_with_comments.ipynb 19.7 KB
36. Advanced Statistical Methods - Logistic Regression/11.3 Bank_data.csv 19.5 KB
36. Advanced Statistical Methods - Logistic Regression/13.1 Bank_data.csv 19.5 KB
36. Advanced Statistical Methods - Logistic Regression/16.1 Bank_data.csv 19.5 KB
36. Advanced Statistical Methods - Logistic Regression/8.3 Bank_data.csv 19.5 KB
17. Statistics - Inferential Statistics Fundamentals/2.2 3.2. What is a distribution_lesson.xlsx 19.5 KB
11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.srt 19.3 KB
15. Statistics - Descriptive Statistics/11.1 2.5. The Histogram_lesson.xlsx 18.6 KB
33/19.1 Multiple Linear Regression with Dummies Exercise Solution.ipynb 18.0 KB
39. Advanced Statistical Methods - Other Types of Clustering/3.1 Heatmaps_with_comments.ipynb 17.7 KB
54/11.3 TensorFlow_MNIST_around_98_percent_accuracy.ipynb 17.7 KB
15. Statistics - Descriptive Statistics/13.2 2.5.The-Histogram-exercise-solution.xlsx 17.1 KB
54/11.5 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb 16.8 KB
34/15.3 SKLEAR~1.IPY 16.8 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.11 TensorFlow_MNIST_All_Exercises.ipynb 16.7 KB
34/12.1 sklearn - Multiple Linear Regression Summary Table_with_comments.ipynb 16.6 KB
34/17.3 sklearn - Feature Scaling Exercise Solution.ipynb 16.3 KB
15. Statistics - Descriptive Statistics/16.2 2.6. Cross table and scatter plot_exercise.xlsx 16.3 KB
18/8.2 3.11. The t-table.xlsx 15.8 KB
18/9.2 3.11.The-t-table.xlsx 15.8 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.10 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb 15.8 KB
12. Probability - Distributions/29.5 Customers_Membership (post).xlsx 15.6 KB
15. Statistics - Descriptive Statistics/13.3 2.5.The-Histogram-exercise.xlsx 15.5 KB
54/10.1 TensorFlow_MNIST_Exercises_All.ipynb 15.5 KB
34/13.1 sklearn - Multiple Linear Regression Exercise Solution.ipynb 15.4 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.4 2. TensorFlow_MNIST_Depth_Solution.ipynb 15.3 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.1 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb 15.3 KB
38. Advanced Statistical Methods - K-Means Clustering/15.2 Species Segmentation with Cluster Analysis Part 2 - Solution.ipynb 15.3 KB
15. Statistics - Descriptive Statistics/7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx 15.2 KB
54/11.8 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb 15.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.6 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb 15.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.7 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb 15.1 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.2 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb 15.1 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.8 TensorFlow_MNIST_around_98_percent_accuracy.ipynb 15.0 KB
34/15.2 sklearn - Feature Selection through Feature Scaling (Standardization) - Part 2.ipynb 14.9 KB
54/11.1 2. TensorFlow_MNIST_Depth_Solution.ipynb 14.9 KB
35/1. Practical Example Linear Regression (Part 1).srt 14.9 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.9 1. TensorFlow_MNIST_Width_Solution.ipynb 14.8 KB
50. Deep Learning - Classifying on the MNIST Dataset/11.5 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb 14.7 KB
20. Statistics - Hypothesis Testing/12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.5 KB
50. Deep Learning - Classifying on the MNIST Dataset/12.2 TensorFlow_MNIST_complete_with_comments.ipynb 14.5 KB
20. Statistics - Hypothesis Testing/15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx 14.4 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11.3 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb 14.4 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/12.3 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb 14.4 KB
54/11.9 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb 14.3 KB
54/11.2 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb 14.3 KB
18/13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx 14.2 KB
54/11.6 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb 14.2 KB
54/11.10 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb 14.1 KB
54/11.11 1. TensorFlow_MNIST_Width_Solution.ipynb 14.0 KB
54/11.4 0. TensorFlow_MNIST_take_note_of_time_Solution.ipynb 14.0 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.4 TensorFlow_Minimal_Example_All_Exercises.ipynb 14.0 KB
10. Probability - Combinatorics/20. A Practical Example of Combinatorics.srt 14.0 KB
54/11.7 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb 13.9 KB
18/13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx 13.7 KB
34/12.3 sklearn - Multiple Linear Regression Summary Table.ipynb 13.7 KB
19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.srt 13.6 KB
62. Appendix - Additional Python Tools/1.3 Additional-Python-Tools-Lectures.ipynb 13.5 KB
62. Appendix - Additional Python Tools/6.1 Additional-Python-Tools-Lectures.ipynb 13.5 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.srt 13.5 KB
33/5.2 Multiple Linear Regression Exercise Solution.ipynb 13.4 KB
15. Statistics - Descriptive Statistics/10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.2 KB
54/9.1 12.9. TensorFlow_MNIST_with_comments.ipynb 13.0 KB
34/10.3 sklearn - Feature Selection with F-regression_with_comments.ipynb 13.0 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Minimal_example_All_Exercises.ipynb 12.9 KB
34/14.2 SKLEAR~1.IPY 12.9 KB
20. Statistics - Hypothesis Testing/15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx 12.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8.2 TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb 12.7 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9.1 TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb 12.7 KB
34/11.1 sklearn - How to properly include p-values.ipynb 12.7 KB
20. Statistics - Hypothesis Testing/13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.6 KB
15. Statistics - Descriptive Statistics/26.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.6 KB
50. Deep Learning - Classifying on the MNIST Dataset/10.1 TensorFlow_MNIST_Part6_with_comments.ipynb 12.5 KB
62. Appendix - Additional Python Tools/5. List Comprehensions.srt 12.4 KB
62. Appendix - Additional Python Tools/1. Using the .format() Method.srt 12.3 KB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.srt 12.3 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9.1 5.6. TensorFlow_Minimal_example_complete.ipynb 12.1 KB
17. Statistics - Inferential Statistics Fundamentals/8.1 3.4.Standard-normal-distribution-exercise.xlsx 12.0 KB
51. Deep Learning - Business Case Example/11.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb 12.0 KB
51. Deep Learning - Business Case Example/12.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb 12.0 KB
2/7. Continuing with BI, ML, and AI.srt 11.9 KB
40. Part 6 Mathematics/16. Why is Linear Algebra Useful.srt 11.8 KB
34/14.3 sklearn - Feature Selection through Feature Scaling (Standardization) - Part 1.ipynb 11.7 KB
36. Advanced Statistical Methods - Logistic Regression/12.2 Accuracy_with_comments.ipynb 11.7 KB
15. Statistics - Descriptive Statistics/26.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.6 KB
54/8.1 12.8. TensorFlow_MNIST_with_comments_Part_6.ipynb 11.5 KB
35/6. Practical Example Linear Regression (Part 4).srt 11.5 KB
15. Statistics - Descriptive Statistics/8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx 11.4 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4.1 Minimal_example_Part_4_Complete.ipynb 11.4 KB
20. Statistics - Hypothesis Testing/20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.4 KB
62. Appendix - Additional Python Tools/1.1 Additional-Python-Tools-Exercises.ipynb 11.4 KB
62. Appendix - Additional Python Tools/6.2 Additional-Python-Tools-Exercises.ipynb 11.4 KB
15. Statistics - Descriptive Statistics/18.1 2.7. Mean, median and mode_exercise_solution.xlsx 11.4 KB
20. Statistics - Hypothesis Testing/13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.3 KB
20. Statistics - Hypothesis Testing/17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.2 KB
20. Statistics - Hypothesis Testing/9.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx 11.2 KB
18/3.2 3.9. Population variance known, z-score_lesson.xlsx 11.2 KB
51. Deep Learning - Business Case Example/4.1 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.2 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.2 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/12.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.2 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.2 KB
18/4.1 3.9. Population variance known, z-score_exercise_solution.xlsx 11.2 KB
18/9.3 3.11. Population variance unknown, t-score_exercise_solution.xlsx 11.1 KB
15. Statistics - Descriptive Statistics/23.2 2.9. Variance_exercise_solution.xlsx 11.1 KB
20. Statistics - Hypothesis Testing/9.2 4.4. Test for the mean. Population variance known_exercise.xlsx 11.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/9.1 TensorFlow_MNIST_Part5_with_comments.ipynb 11.0 KB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.srt 11.0 KB
15. Statistics - Descriptive Statistics/24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx 11.0 KB
20. Statistics - Hypothesis Testing/8.1 4.4. Test for the mean. Population variance known_lesson.xlsx 11.0 KB
24. Python - Basic Python Syntax/12.1 Structure Your Code with Indentation - Lecture_Py3.ipynb 10.9 KB
15. Statistics - Descriptive Statistics/18.2 2.7. Mean, median and mode_exercise.xlsx 10.9 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).srt 10.9 KB
18/4.2 3.9. Population variance known, z-score_exercise.xlsx 10.8 KB
15. Statistics - Descriptive Statistics/23.1 2.9. Variance_exercise.xlsx 10.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting Acquainted with the Dataset.srt 10.8 KB
18/8.1 3.11. Population variance unknown, t-score_lesson.xlsx 10.8 KB
20. Statistics - Hypothesis Testing/17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.8 KB
38. Advanced Statistical Methods - K-Means Clustering/15.4 Species Segmentation with Cluster Analysis Part 2 - Exercise.ipynb 10.7 KB
51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.srt 10.7 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8.1 TensorFlow_Audiobooks_optimizing_the_algorithm.ipynb 10.6 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9.2 TensorFlow_Audiobooks_optimizing_the_algorithm.ipynb 10.6 KB
2/5. Business Analytics, Data Analytics, and Data Science An Introduction.srt 10.6 KB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.srt 10.6 KB
18/9.1 3.11. Population variance unknown, t-score_exercise.xlsx 10.6 KB
35/8. Practical Example Linear Regression (Part 5).srt 10.6 KB
20. Statistics - Hypothesis Testing/20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.5 KB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.srt 10.5 KB
15. Statistics - Descriptive Statistics/17.1 2.7. Mean, median and mode_lesson.xlsx 10.5 KB
50. Deep Learning - Classifying on the MNIST Dataset/8.1 TensorFlow_MNIST_Part4_with_comments.ipynb 10.5 KB
18/12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx 10.5 KB
34/10.1 sklearn - Feature Selection with F-regression.ipynb 10.4 KB
34/8.1 sklearn - Multiple Linear Regression and Adjusted R-squared_with_comments.ipynb 10.4 KB
56. Software Integration/5. Taking a Closer Look at APIs.srt 10.4 KB
17. Statistics - Inferential Statistics Fundamentals/6.1 3.4. Standard normal distribution_lesson.xlsx 10.4 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7.1 TensorFlow_Audiobooks_Outlining_the_model_with_comments.ipynb 10.3 KB
38. Advanced Statistical Methods - K-Means Clustering/5.1 Categorical.csv 10.3 KB
34/9.2 sklearn - Multiple Linear Regression and Adjusted R-squared - Exercise Solution.ipynb 10.3 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.srt 10.2 KB
54/8. MNIST Learning.srt 10.2 KB
18/15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx 10.1 KB
15. Statistics - Descriptive Statistics/22.1 2.9. Variance_lesson.xlsx 10.1 KB
51. Deep Learning - Business Case Example/9.1 TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments.ipynb 10.1 KB
51. Deep Learning - Business Case Example/5.1 TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb 10.0 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/5.2 TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb 10.0 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.srt 10.0 KB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.srt 10.0 KB
62. Appendix - Additional Python Tools/6. Anonymous (Lambda) Functions.srt 9.9 KB
34/9.3 sklearn - Multiple Linear Regression and Adjusted R-squared - Exercise.ipynb 9.8 KB
13. Probability - Probability in Other Fields/1. Probability in Finance.srt 9.8 KB
18/14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx 9.8 KB
28. Python - Sequences/1. Lists.srt 9.8 KB
18/15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx 9.8 KB
18/3. Confidence Intervals; Population Variance Known; Z-score.srt 9.8 KB
18/17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx 9.8 KB
20. Statistics - Hypothesis Testing/14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx 9.8 KB
12. Probability - Distributions/29.1 Customers_Membership.xlsx 9.7 KB
20. Statistics - Hypothesis Testing/16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx 9.6 KB
34/19. Train - Test Split Explained.srt 9.6 KB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.srt 9.6 KB
61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.srt 9.5 KB
12. Probability - Distributions/29.2 Daily Views.xlsx 9.5 KB
18/16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx 9.5 KB
40. Part 6 Mathematics/15. Dot Product of Matrices.srt 9.5 KB
15. Statistics - Descriptive Statistics/21.2 2.8. Skewness_exercise.xlsx 9.5 KB
33/20.2 Making predictions_with_comments.ipynb 9.4 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7.2 TensorFlow_Audiobooks_Outlining_the_model.ipynb 9.4 KB
12. Probability - Distributions/3. Types of Probability Distributions.srt 9.3 KB
20. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx 9.3 KB
50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.srt 9.3 KB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).srt 9.2 KB
18/17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx 9.2 KB
34/8.2 sklearn - Multiple Linear Regression and Adjusted R-squared.ipynb 9.1 KB
54/4. MNIST Model Outline.srt 9.1 KB
44. Deep Learning - TensorFlow 2.0 Introduction/6.1 TensorFlow_Minimal_example_Part2.ipynb 9.1 KB
34/19.1 sklearn - Train Test Split_with_comments.ipynb 9.0 KB
3/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt 9.0 KB
9. Part 2 Probability/1. The Basic Probability Formula.srt 8.9 KB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.srt 8.8 KB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.srt 8.7 KB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.srt 8.7 KB
12. Probability - Distributions/15. Characteristics of Continuous Distributions.srt 8.7 KB
34/7.2 sklearn - Multiple Linear Regression_with_comments.ipynb 8.7 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8.1 5.5. TensorFlow_Minimal_example_Part_3.ipynb 8.6 KB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.srt 8.6 KB
50. Deep Learning - Classifying on the MNIST Dataset/7.1 TensorFlow_MNIST_Part3_with_comments.ipynb 8.6 KB
51. Deep Learning - Business Case Example/5.2 TensorFlow_Audiobooks_Preprocessing_Exercise.ipynb 8.6 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/5.1 TensorFlow_Audiobooks_Preprocessing_Exercise.ipynb 8.6 KB
56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.srt 8.5 KB
54/7.1 12.7. TensorFlow_MNIST_with_comments_Part_5.ipynb 8.5 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/32.1 Absenteeism Exercise - Preprocessing - df_preprocessed.ipynb 8.5 KB
38. Advanced Statistical Methods - K-Means Clustering/7.2 How to Choose the Number of Clusters - Solution.ipynb 8.5 KB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.srt 8.5 KB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.srt 8.5 KB
13. Probability - Probability in Other Fields/2. Probability in Statistics.srt 8.4 KB
28. Python - Sequences/7. Dictionaries.srt 8.4 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.srt 8.4 KB
59/2. Creating the Targets for the Logistic Regression.srt 8.4 KB
28. Python - Sequences/3. Using Methods.srt 8.4 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/29.1 Absenteeism Exercise - Removing the Date Column - SOLUTION.ipynb 8.3 KB
12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.srt 8.3 KB
62. Appendix - Additional Python Tools/3. Introduction to Nested For Loops.srt 8.3 KB
36. Advanced Statistical Methods - Logistic Regression/16.3 Bank_data_testing.csv 8.3 KB
38. Advanced Statistical Methods - K-Means Clustering/3.3 Countries-exercise.csv 8.3 KB
38. Advanced Statistical Methods - K-Means Clustering/7.3 Countries_exercise.csv 8.3 KB
54/9. MNIST Results and Testing.srt 8.2 KB
33/18. Dealing with Categorical Data - Dummy Variables.srt 8.1 KB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.srt 8.1 KB
59/5. Splitting the Data for Training and Testing.srt 8.1 KB
18/12. Confidence intervals. Two means. Dependent samples.srt 8.0 KB
35/2. Practical Example Linear Regression (Part 2).srt 8.0 KB
62. Appendix - Additional Python Tools/4. Triple Nested For Loops.srt 8.0 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.srt 8.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.srt 7.9 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.srt 7.9 KB
29. Python - Iterations/8. How to Iterate over Dictionaries.srt 7.9 KB
32/8. First Regression in Python.srt 7.9 KB
54/6.1 12.6. TensorFlow_MNIST_with_comments_Part_4.ipynb 7.9 KB
59/8. Interpreting the Coefficients for Our Problem.srt 7.9 KB
44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.srt 7.8 KB
51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.srt 7.8 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.srt 7.8 KB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.srt 7.8 KB
34/7.3 sklearn - Multiple Linear Regression.ipynb 7.8 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.srt 7.8 KB
34/14. Feature Scaling (Standardization).srt 7.7 KB
29. Python - Iterations/4. Lists with the range() Function.srt 7.7 KB
36. Advanced Statistical Methods - Logistic Regression/15.3 Testing the model_with_comments.ipynb 7.6 KB
23. Python - Variables and Data Types/5.3 Strings - Lecture_Py3.ipynb 7.6 KB
12. Probability - Distributions/1. Fundamentals of Probability Distributions.srt 7.5 KB
15. Statistics - Descriptive Statistics/22. Variance.srt 7.5 KB
60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.srt 7.5 KB
33/3. Adjusted R-Squared.srt 7.5 KB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).srt 7.5 KB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.srt 7.5 KB
38. Advanced Statistical Methods - K-Means Clustering/6.2 Selecting the number of clusters_with_comments.ipynb 7.5 KB
29. Python - Iterations/6. Conditional Statements and Loops.srt 7.5 KB
59/6. Fitting the Model and Assessing its Accuracy.srt 7.4 KB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.srt 7.4 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt 7.4 KB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.srt 7.4 KB
38. Advanced Statistical Methods - K-Means Clustering/14.2 Species Segmentation with Cluster Analysis Part 1- Solution.ipynb 7.4 KB
34/3. Simple Linear Regression with sklearn.srt 7.3 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/29.2 Absenteeism Exercise - Preprocessing - ChP - df_date_reason_mod.ipynb 7.3 KB
54/5.1 12.5. TensorFlow_MNIST_with_comments_Part_3.ipynb 7.3 KB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.srt 7.3 KB
34/15. Feature Selection through Standardization of Weights.srt 7.3 KB
59/10. Interpreting the Coefficients of the Logistic Regression.srt 7.2 KB
34/19.2 sklearn - Train Test Split.ipynb 7.2 KB
61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.srt 7.2 KB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.srt 7.2 KB
11. Probability - Bayesian Inference/20. Bayes' Law.srt 7.2 KB
23. Python - Variables and Data Types/5. Python Strings.srt 7.1 KB
33/18.3 Dummy variables_with_comments.ipynb 7.1 KB
32/1. The Linear Regression Model.srt 7.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.srt 7.0 KB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.srt 7.0 KB
22. Part 4 Introduction to Python/3. Why Python.srt 7.0 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.srt 6.9 KB
28. Python - Sequences/6. Tuples.srt 6.9 KB
22. Part 4 Introduction to Python/1. Introduction to Programming.srt 6.9 KB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.srt 6.9 KB
38. Advanced Statistical Methods - K-Means Clustering/12.2 Market segmentation example_Part2_with_comments.ipynb 6.8 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Minimal_example_Part_3.ipynb 6.8 KB
36. Advanced Statistical Methods - Logistic Regression/16.2 Testing the Model - Exercise..ipynb 6.8 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).srt 6.8 KB
50. Deep Learning - Classifying on the MNIST Dataset/12.1 TensorFlow_MNIST_complete.ipynb 6.8 KB
9. Part 2 Probability/7. Events and Their Complements.srt 6.7 KB
56. Software Integration/9. Software Integration - Explained.srt 6.7 KB
45/3. Digging into a Deep Net.srt 6.7 KB
34/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt 6.7 KB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.srt 6.7 KB
9. Part 2 Probability/3. Computing Expected Values.srt 6.7 KB
33/13. A3 Normality and Homoscedasticity.srt 6.7 KB
34/10. Feature Selection (F-regression).srt 6.7 KB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.srt 6.7 KB
13. Probability - Probability in Other Fields/3. Probability in Data Science.srt 6.6 KB
2/1. Data Science and Business Buzzwords Why are there so Many.srt 6.6 KB
59/7. Creating a Summary Table with the Coefficients and Intercept.srt 6.6 KB
60. Case Study - Loading the 'absenteeism_module'/1.5 absenteeism_module.py 6.6 KB
26. Python - Conditional Statements/4. The ELIF Statement.srt 6.6 KB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.srt 6.6 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.srt 6.6 KB
29. Python - Iterations/1. For Loops.srt 6.6 KB
32/17. R-Squared.srt 6.6 KB
12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.srt 6.6 KB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.srt 6.5 KB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason Behind These Disciplines.srt 6.5 KB
59/12. Testing the Model We Created.srt 6.5 KB
52. Deep Learning - Conclusion/4. An overview of CNNs.srt 6.4 KB
9. Part 2 Probability/5. Frequency.srt 6.4 KB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.srt 6.4 KB
50. Deep Learning - Classifying on the MNIST Dataset/5.1 TensorFlow_MNIST_Part2_with_comments.ipynb 6.4 KB
44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.srt 6.4 KB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt 6.4 KB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.srt 6.3 KB
32/11. How to Interpret the Regression Table.srt 6.3 KB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.srt 6.3 KB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt 6.3 KB
34/8. Calculating the Adjusted R-Squared in sklearn.srt 6.3 KB
51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.srt 6.3 KB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.srt 6.3 KB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.srt 6.2 KB
44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.srt 6.2 KB
36. Advanced Statistical Methods - Logistic Regression/5.3 Example_bank_data.csv 6.2 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7.1 5.4. TensorFlow_Minimal_example_Part_2.ipynb 6.2 KB
28. Python - Sequences/7.3 Dictionaries - Solution_Py3.ipynb 6.2 KB
18/10. Margin of Error.srt 6.1 KB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.srt 6.1 KB
54/4.1 12.4. TensorFlow_MNIST_with_comments_Part_2.ipynb 6.1 KB
30. Python - Advanced Python Tools/1. Object Oriented Programming.srt 6.1 KB
18/14. Confidence intervals. Two means. Independent Samples (Part 1).srt 6.1 KB
34/17.2 sklearn - Feature Scaling Exercise.ipynb 6.1 KB
34/3.2 sklearn - Simple Linear Regression_with_comments.ipynb 6.1 KB
62. Appendix - Additional Python Tools/2. Iterating Over Range Objects.srt 6.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.srt 6.0 KB
49. Deep Learning - Preprocessing/3. Standardization.srt 6.0 KB
15. Statistics - Descriptive Statistics/1. Types of Data.srt 6.0 KB
48/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt 5.9 KB
56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.srt 5.9 KB
29. Python - Iterations/3. While Loops and Incrementing.srt 5.9 KB
38. Advanced Statistical Methods - K-Means Clustering/11.3 Market segmentation example_with_comments.ipynb 5.9 KB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.srt 5.9 KB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.srt 5.9 KB
25. Python - Other Python Operators/3.1 Logical and Identity Operators - Lecture_Py3.ipynb 5.9 KB
25. Python - Other Python Operators/3.2 Logical and Identity Operators - Lecture_Py3.ipynb 5.9 KB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.srt 5.9 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.srt 5.8 KB
38. Advanced Statistical Methods - K-Means Clustering/2.2 Country clusters_with_comments.ipynb 5.8 KB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.srt 5.8 KB
33/20.1 Making predictions.ipynb 5.8 KB
36. Advanced Statistical Methods - Logistic Regression/15.2 Testing the model.ipynb 5.8 KB
25. Python - Other Python Operators/3. Logical and Identity Operators.srt 5.8 KB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.srt 5.7 KB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.srt 5.7 KB
18/8. Confidence Intervals; Population Variance Unknown; T-score.srt 5.7 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.srt 5.7 KB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.srt 5.7 KB
34/13.2 sklearn - Multiple Linear Regression Exercise.ipynb 5.7 KB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.srt 5.7 KB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.srt 5.6 KB
32/7. Python Packages Installation.srt 5.6 KB
38. Advanced Statistical Methods - K-Means Clustering/4.1 Categorical data_with_comments.ipynb 5.6 KB
59/16. Preparing the Deployment of the Model through a Module.srt 5.6 KB
10. Probability - Combinatorics/11. Solving Combinations.srt 5.6 KB
34/16. Predicting with the Standardized Coefficients.srt 5.6 KB
46. Deep Learning - Overfitting/1. What is Overfitting.srt 5.6 KB
51. Deep Learning - Business Case Example/4.2 TensorFlow_Audiobooks_Preprocessing.ipynb 5.6 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4.1 TensorFlow_Audiobooks_Preprocessing.ipynb 5.6 KB
59/13. Saving the Model and Preparing it for Deployment.srt 5.6 KB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.srt 5.6 KB
28. Python - Sequences/5. List Slicing.srt 5.5 KB
38. Advanced Statistical Methods - K-Means Clustering/7.1 How to Choose the Number of Clusters - Exercise.ipynb 5.5 KB
11. Probability - Bayesian Inference/7. Union of Sets.srt 5.5 KB
27. Python - Python Functions/7.3 Notable Built-In Functions in Python - Solution_Py3.ipynb 5.5 KB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent Samples (Part 1).srt 5.5 KB
56. Software Integration/7. Communication between Software Products through Text Files.srt 5.5 KB
14. Part 3 Statistics/1. Population and Sample.srt 5.5 KB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.srt 5.5 KB
57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt 5.5 KB
23. Python - Variables and Data Types/5.1 Strings - Solution_Py3.ipynb 5.5 KB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.srt 5.4 KB
18/5. Confidence Interval Clarifications.srt 5.4 KB
36. Advanced Statistical Methods - Logistic Regression/13.3 Calculating the Accuracy of the Model - Exercise.ipynb 5.4 KB
40. Part 6 Mathematics/13. Transpose of a Matrix.srt 5.4 KB
36. Advanced Statistical Methods - Logistic Regression/2.3 Admittance_with_comments.ipynb 5.3 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.srt 5.3 KB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.srt 5.3 KB
12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.srt 5.3 KB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.srt 5.3 KB
45/5. Activation Functions.srt 5.3 KB
33/11. A2 No Endogeneity.srt 5.2 KB
59/11. Backward Elimination or How to Simplify Your Model.srt 5.2 KB
44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.srt 5.2 KB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.srt 5.2 KB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.srt 5.2 KB
48/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt 5.2 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.srt 5.2 KB
54/6. Calculating the Accuracy of the Model.srt 5.2 KB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent Samples (Part 2).srt 5.1 KB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.srt 5.1 KB
2/9. A Breakdown of our Data Science Infographic.srt 5.1 KB
1. Part 1 Introduction/2. What Does the Course Cover.srt 5.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.srt 5.1 KB
2/3. What is the difference between Analysis and Analytics.srt 5.1 KB
11. Probability - Bayesian Inference/1. Sets and Events.srt 5.1 KB
20. Statistics - Hypothesis Testing/10. p-value.srt 5.0 KB
59/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt 5.0 KB
28. Python - Sequences/5.3 List Slicing - Lecture_Py3.ipynb 5.0 KB
12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.srt 5.0 KB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.srt 5.0 KB
11. Probability - Bayesian Inference/13. The Conditional Probability Formula.srt 4.9 KB
15. Statistics - Descriptive Statistics/27. Covariance.srt 4.9 KB
34/3.1 sklearn - Simple Linear Regression.ipynb 4.9 KB
33/14. A4 No Autocorrelation.srt 4.9 KB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.srt 4.9 KB
38. Advanced Statistical Methods - K-Means Clustering/5.3 Clustering Categorical Data - Solution.ipynb 4.9 KB
46. Deep Learning - Overfitting/3. What is Validation.srt 4.9 KB
36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.srt 4.9 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.srt 4.8 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/23.1 Absenteeism Exercise - Preprocessing - df_reason_mod.ipynb 4.8 KB
30. Python - Advanced Python Tools/7. Importing Modules in Python.srt 4.8 KB
48/1. Stochastic Gradient Descent.srt 4.8 KB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.srt 4.8 KB
37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.srt 4.8 KB
36. Advanced Statistical Methods - Logistic Regression/8.2 Understanding Logistic Regression Tables - Solution.ipynb 4.8 KB
36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.srt 4.8 KB
12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.srt 4.8 KB
60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.srt 4.8 KB
15. Statistics - Descriptive Statistics/30. Correlation Coefficient.srt 4.7 KB
51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.srt 4.7 KB
38. Advanced Statistical Methods - K-Means Clustering/12.1 Market segmentation example_Part2.ipynb 4.7 KB
39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.srt 4.7 KB
38. Advanced Statistical Methods - K-Means Clustering/3.1 A Simple Example of Clustering - Solution.ipynb 4.6 KB
22. Part 4 Introduction to Python/5. Why Jupyter.srt 4.6 KB
41. Part 7 Deep Learning/1. What to Expect from this Part.srt 4.6 KB
11. Probability - Bayesian Inference/18. The Multiplication Law.srt 4.6 KB
33/16. A5 No Multicollinearity.srt 4.6 KB
33/18.2 Dummy Variables.ipynb 4.6 KB
51. Deep Learning - Business Case Example/7.1 TensorFlow_Audiobooks_Machine_Learning_Part1_with_comments.ipynb 4.6 KB
38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.srt 4.6 KB
28. Python - Sequences/6.2 Tuples - Solution_Py3.ipynb 4.6 KB
59/1. Exploring the Problem with a Machine Learning Mindset.srt 4.6 KB
40. Part 6 Mathematics/7.1 Scalars, Vectors, and Matrices.ipynb 4.5 KB
15. Statistics - Descriptive Statistics/3. Levels of Measurement.srt 4.5 KB
38. Advanced Statistical Methods - K-Means Clustering/6.1 Selecting the number of clusters.ipynb 4.5 KB
23. Python - Variables and Data Types/1. Variables.srt 4.5 KB
10. Probability - Combinatorics/9. Solving Variations without Repetition.srt 4.5 KB
27. Python - Python Functions/7.2 Notable Built-In Functions in Python - Lecture_Py3.ipynb 4.5 KB
36. Advanced Statistical Methods - Logistic Regression/11.1 Binary Predictors in a Logistic Regression - Solution.ipynb 4.5 KB
18/16. Confidence intervals. Two means. Independent Samples (Part 2).srt 4.5 KB
51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.srt 4.5 KB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.srt 4.5 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.srt 4.5 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.srt 4.5 KB
45/7. Backpropagation.srt 4.5 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).srt 4.5 KB
38. Advanced Statistical Methods - K-Means Clustering/14.3 Species Segmentation with Cluster Analysis Part 1- Exercise.ipynb 4.5 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).srt 4.5 KB
45/6. Activation Functions Softmax Activation.srt 4.5 KB
33/20. Making Predictions with the Linear Regression.srt 4.4 KB
36. Advanced Statistical Methods - Logistic Regression/5.1 Building a Logistic Regression - Solution.ipynb 4.4 KB
11. Probability - Bayesian Inference/3. Ways Sets Can Interact.srt 4.4 KB
28. Python - Sequences/3.3 Help Yourself with Methods - Lecture_Py3.ipynb 4.4 KB
15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.srt 4.4 KB
28. Python - Sequences/7.2 Dictionaries - Lecture_Py3.ipynb 4.4 KB
40. Part 6 Mathematics/1. What is a Matrix.srt 4.3 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.srt 4.3 KB
10. Probability - Combinatorics/13. Symmetry of Combinations.srt 4.3 KB
27. Python - Python Functions/2. How to Create a Function with a Parameter.srt 4.3 KB
42. Deep Learning - Introduction to Neural Networks/3. Training the Model.srt 4.3 KB
40. Part 6 Mathematics/14. Dot Product.srt 4.3 KB
28. Python - Sequences/5.1 List Slicing - Solution_Py3.ipynb 4.3 KB
24. Python - Basic Python Syntax/1.2 Arithmetic Operators - Solution_Py3.ipynb 4.2 KB
27. Python - Python Functions/7. Built-in Functions in Python.srt 4.2 KB
59/4. Standardizing the Data.srt 4.2 KB
46. Deep Learning - Overfitting/5. N-Fold Cross Validation.srt 4.2 KB
34/7. Multiple Linear Regression with sklearn.srt 4.2 KB
32/13. Decomposition of Variability.srt 4.2 KB
10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.srt 4.1 KB
57. Case Study - What's Next in the Course/3. Introducing the Data Set.srt 4.1 KB
12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.srt 4.1 KB
18/6. Student's T Distribution.srt 4.1 KB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.srt 4.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/32.2 Absenteeism Exercise - EXERCISES and SOLUTIONS.ipynb 4.1 KB
35/4. Practical Example Linear Regression (Part 3).srt 4.1 KB
24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.srt 4.1 KB
44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.srt 4.1 KB
36. Advanced Statistical Methods - Logistic Regression/4.3 Admittance regression tables_fixed_error.ipynb 4.1 KB
40. Part 6 Mathematics/5. Linear Algebra and Geometry.srt 4.1 KB
10. Probability - Combinatorics/3. Permutations and How to Use Them.srt 4.1 KB
32/8.2 Simple linear regression_with_comments.ipynb 4.1 KB
37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.srt 4.1 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.srt 4.1 KB
40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.srt 4.0 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.srt 4.0 KB
45/8. Backpropagation Picture.srt 4.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/3.1 TensorFlow_MNIST_Part1_with_comments.ipynb 4.0 KB
17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.srt 3.9 KB
54/3.1 12.3. TensorFlow_MNIST_with_comments_Part_1.ipynb 3.9 KB
45/4. Non-Linearities and their Purpose.srt 3.9 KB
42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).srt 3.9 KB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.srt 3.9 KB
12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.srt 3.9 KB
32/15. What is the OLS.srt 3.8 KB
38. Advanced Statistical Methods - K-Means Clustering/11.2 Market segmentation example.ipynb 3.8 KB
32/8.1 Simple linear regression.ipynb 3.8 KB
23. Python - Variables and Data Types/1.2 Variables - Solution_Py3.ipynb 3.8 KB
38. Advanced Statistical Methods - K-Means Clustering/5.2 Clustering Categorical Data - Exercise.ipynb 3.8 KB
40. Part 6 Mathematics/3. Scalars and Vectors.srt 3.8 KB
57. Case Study - What's Next in the Course/2. The Business Task.srt 3.7 KB
10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.srt 3.7 KB
22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.srt 3.7 KB
10. Probability - Combinatorics/19. A Recap of Combinatorics.srt 3.7 KB
17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.srt 3.7 KB
47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt 3.7 KB
52. Deep Learning - Conclusion/5. An Overview of RNNs.srt 3.7 KB
23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.srt 3.7 KB
47. Deep Learning - Initialization/2. Types of Simple Initializations.srt 3.7 KB
27. Python - Python Functions/7.1 Notable Built-In Functions in Python - Exercise_Py3.ipynb 3.7 KB
59/3. Selecting the Inputs for the Logistic Regression.srt 3.7 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Minimal_example_Part_2.ipynb 3.7 KB
15. Statistics - Descriptive Statistics/19. Skewness.srt 3.6 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.srt 3.6 KB
44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.srt 3.6 KB
36. Advanced Statistical Methods - Logistic Regression/12.1 Accuracy.ipynb 3.6 KB
38. Advanced Statistical Methods - K-Means Clustering/15.3 iris_with_answers.csv 3.6 KB
38. Advanced Statistical Methods - K-Means Clustering/3.2 A Simple Example of Clustering - Exercise.ipynb 3.6 KB
54/2. MNIST How to Tackle the MNIST.srt 3.6 KB
23. Python - Variables and Data Types/1.3 Variables - Lecture_Py3.ipynb 3.6 KB
40. Part 6 Mathematics/8. What is a Tensor.srt 3.6 KB
40. Part 6 Mathematics/15.1 Dot product (Part 2).ipynb 3.6 KB
46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.srt 3.6 KB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.srt 3.6 KB
50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.srt 3.6 KB
32/9.2 Simple Linear Regression Exercise Solution.ipynb 3.6 KB
30. Python - Advanced Python Tools/5. What is the Standard Library.srt 3.6 KB
36. Advanced Statistical Methods - Logistic Regression/2.2 Admittance.ipynb 3.5 KB
54/5. MNIST Loss and Optimization Algorithm.srt 3.5 KB
26. Python - Conditional Statements/1. The IF Statement.srt 3.5 KB
24. Python - Basic Python Syntax/1.1 Arithmetic Operators - Lecture_Py3.ipynb 3.5 KB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.srt 3.5 KB
27. Python - Python Functions/5. Conditional Statements and Functions.srt 3.5 KB
47. Deep Learning - Initialization/1. What is Initialization.srt 3.5 KB
44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.srt 3.5 KB
11. Probability - Bayesian Inference/15. The Law of Total Probability.srt 3.5 KB
54/1. MNIST What is the MNIST Dataset.srt 3.5 KB
10. Probability - Combinatorics/7. Solving Variations with Repetition.srt 3.5 KB
11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.srt 3.5 KB
34/18. Underfitting and Overfitting.srt 3.5 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.srt 3.4 KB
48/3. Momentum.srt 3.4 KB
25. Python - Other Python Operators/3.3 Logical and Identity Operators - Solution_Py3.ipynb 3.4 KB
34/1. What is sklearn and How is it Different from Other Packages.srt 3.4 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.srt 3.4 KB
33/19.3 real_estate_price_size_year_view.csv 3.4 KB
23. Python - Variables and Data Types/3.2 Numbers and Boolean Values - Lecture_Py3.ipynb 3.4 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6.1 5.3. TensorFlow_Minimal_example_Part_1.ipynb 3.4 KB
33/1. Multiple Linear Regression.srt 3.3 KB
38. Advanced Statistical Methods - K-Means Clustering/4.2 Categorical data.ipynb 3.3 KB
48/7. Adam (Adaptive Moment Estimation).srt 3.3 KB
38. Advanced Statistical Methods - K-Means Clustering/2.1 Country clusters.ipynb 3.3 KB
27. Python - Python Functions/3.3 Another Way to Define a Function - Lecture_Py3.ipynb 3.3 KB
36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.srt 3.3 KB
37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.srt 3.3 KB
10. Probability - Combinatorics/5. Simple Operations with Factorials.srt 3.3 KB
18/1. What are Confidence Intervals.srt 3.3 KB
26. Python - Conditional Statements/4.2 Else If, for Brief - Elif - Lecture_Py3.ipynb 3.2 KB
45/2. What is a Deep Net.srt 3.2 KB
38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.srt 3.2 KB
23. Python - Variables and Data Types/3.1 Numbers and Boolean Values - Solution_Py3.ipynb 3.2 KB
40. Part 6 Mathematics/10.1 Adding and subtracting matrices.ipynb 3.2 KB
36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.srt 3.2 KB
28. Python - Sequences/1.2 Lists - Solution_Py3.ipynb 3.2 KB
40. Part 6 Mathematics/12.1 Errors when adding scalars, vectors, and matrices in Python.ipynb 3.2 KB
36. Advanced Statistical Methods - Logistic Regression/8.1 Understanding Logistic Regression Tables - Exercise.ipynb 3.2 KB
26. Python - Conditional Statements/3. The ELSE Statement.srt 3.1 KB
42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.srt 3.1 KB
24. Python - Basic Python Syntax/5.3 Reassign Values - Lecture_Py3.ipynb 3.1 KB
50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.srt 3.1 KB
33/7. OLS Assumptions.srt 3.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.srt 3.0 KB
33/19.2 Multiple Linear Regression with Dummies Exercise.ipynb 3.0 KB
34/12. Creating a Summary Table with P-values.srt 3.0 KB
15. Statistics - Descriptive Statistics/11. The Histogram.srt 3.0 KB
29. Python - Iterations/6.2 Use Conditional Statements and Loops Together - Solution_Py3.ipynb 3.0 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.srt 2.9 KB
28. Python - Sequences/7.1 Dictionaries - Exercise_Py3.ipynb 2.9 KB
34/2. How are we Going to Approach this Section.srt 2.9 KB
54/7. MNIST Batching and Early Stopping.srt 2.9 KB
36. Advanced Statistical Methods - Logistic Regression/5.2 Building a Logistic Regression - Exercise.ipynb 2.9 KB
26. Python - Conditional Statements/5. A Note on Boolean Values.srt 2.9 KB
28. Python - Sequences/6.3 Tuples - Lecture_Py3.ipynb 2.9 KB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).srt 2.9 KB
40. Part 6 Mathematics/13.1 Tranpose of a matrix.ipynb 2.9 KB
27. Python - Python Functions/3. Defining a Function in Python - Part II.srt 2.9 KB
29. Python - Iterations/8.1 Iterating over Dictionaries - Solution_Py3.ipynb 2.9 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/5. What's Regression Analysis - a Quick Refresher.html 2.8 KB
48/2. Problems with Gradient Descent.srt 2.8 KB
28. Python - Sequences/3.2 Help Yourself with Methods - Solution_Py3.ipynb 2.8 KB
33/3.3 Multiple linear regression and Adjusted R-squared_with_comments.ipynb 2.8 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.srt 2.8 KB
12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.srt 2.8 KB
28. Python - Sequences/5.2 List Slicing - Exercise_Py3.ipynb 2.8 KB
32/9.1 Simple Linear Regression Exercise.ipynb 2.8 KB
42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.srt 2.8 KB
49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.srt 2.8 KB
12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.srt 2.8 KB
11. Probability - Bayesian Inference/16. The Additive Rule.srt 2.7 KB
12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.srt 2.7 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.srt 2.7 KB
28. Python - Sequences/1.1 Lists - Lecture_Py3.ipynb 2.7 KB
42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.srt 2.7 KB
46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.srt 2.6 KB
24. Python - Basic Python Syntax/1.3 Arithmetic Operators - Exercise_Py3.ipynb 2.6 KB
23. Python - Variables and Data Types/5.2 Strings - Exercise_Py3.ipynb 2.6 KB
40. Part 6 Mathematics/12. Errors when Adding Matrices.srt 2.6 KB
36. Advanced Statistical Methods - Logistic Regression/10.1 2.02. Binary predictors.csv 2.6 KB
33/6. Test for Significance of the Model (F-Test).srt 2.6 KB
52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.srt 2.5 KB
36. Advanced Statistical Methods - Logistic Regression/11.2 Binary Predictors in a Logistic Regression - Exercise.ipynb 2.5 KB
63. Bonus Lecture/1. Bonus Lecture Next Steps.html 2.5 KB
25. Python - Other Python Operators/1.2 Comparison Operators - Lecture_Py3.ipynb 2.5 KB
55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.srt 2.5 KB
11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.srt 2.5 KB
36. Advanced Statistical Methods - Logistic Regression/4.1 Admittance regression_summary_error.ipynb 2.5 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/1. What to Expect from the Following Sections.html 2.5 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.srt 2.5 KB
11. Probability - Bayesian Inference/5. Intersection of Sets.srt 2.5 KB
25. Python - Other Python Operators/1. Comparison Operators.srt 2.5 KB
12. Probability - Distributions/5. Characteristics of Discrete Distributions.srt 2.5 KB
33/5.3 Multiple Linear Regression Exercise.ipynb 2.5 KB
27. Python - Python Functions/1. Defining a Function in Python.srt 2.4 KB
36. Advanced Statistical Methods - Logistic Regression/10.2 Binary predictors.ipynb 2.4 KB
29. Python - Iterations/7. Conditional Statements, Functions, and Loops.srt 2.4 KB
25. Python - Other Python Operators/1.3 Comparison Operators - Solution_Py3.ipynb 2.4 KB
38. Advanced Statistical Methods - K-Means Clustering/14.1 iris_dataset.csv 2.4 KB
38. Advanced Statistical Methods - K-Means Clustering/15.1 iris_dataset.csv 2.4 KB
26. Python - Conditional Statements/4.1 Else If, for Brief - Elif - Solution_Py3.ipynb 2.4 KB
45/1. What is a Layer.srt 2.4 KB
33/9. A1 Linearity.srt 2.4 KB
33/5.1 real_estate_price_size_year.csv 2.4 KB
34/13.3 real_estate_price_size_year.csv 2.4 KB
34/17.1 real_estate_price_size_year.csv 2.4 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/14. Dropping a Dummy Variable from the Data Set.html 2.3 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/3. A Note on Installing Packages in Anaconda.html 2.3 KB
20. Statistics - Hypothesis Testing/2. Further Reading on Null and Alternative Hypothesis.html 2.3 KB
23. Python - Variables and Data Types/3.3 Numbers and Boolean Values - Exercise_Py3.ipynb 2.3 KB
29. Python - Iterations/4.3 Create Lists with the range() Function - Solution_Py3.ipynb 2.3 KB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.srt 2.2 KB
23. Python - Variables and Data Types/1.4 Variables - Exercise_Py3.ipynb 2.2 KB
31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.srt 2.2 KB
26. Python - Conditional Statements/1.1 Introduction to the If Statement - Solution_Py3.ipynb 2.2 KB
54/11. MNIST Solutions.html 2.2 KB
29. Python - Iterations/8.2 Iterating over Dictionaries - Exercise_Py3.ipynb 2.2 KB
38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.srt 2.2 KB
24. Python - Basic Python Syntax/12. Structuring with Indentation.srt 2.2 KB
24. Python - Basic Python Syntax/10.3 Indexing Elements - Solution_Py3.ipynb 2.2 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.srt 2.2 KB
48/5. Learning Rate Schedules Visualized.srt 2.2 KB
33/3.2 Multiple linear regression and Adjusted R-squared_.ipynb 2.1 KB
59/14. ARTICLE - A Note on 'pickling'.html 2.1 KB
28. Python - Sequences/1.3 Lists - Exercise_Py3.ipynb 2.1 KB
40. Part 6 Mathematics/14.1 Dot product.ipynb 2.1 KB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).srt 2.1 KB
54/10. MNIST Exercises.html 2.1 KB
24. Python - Basic Python Syntax/5.2 Reassign Values - Solution_Py3.ipynb 2.1 KB
42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.srt 2.1 KB
54/3. MNIST Relevant Packages.srt 2.1 KB
61. Case Study - Analyzing the Predicted Outputs in Tableau/1.1 Absenteeism_predictions.csv 2.1 KB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2.1 Absenteeism_predictions.csv 2.1 KB
29. Python - Iterations/6.1 Use Conditional Statements and Loops Together - Exercise_Py3.ipynb 2.1 KB
32/3. Correlation vs Regression.srt 2.1 KB
36. Advanced Statistical Methods - Logistic Regression/4.2 Admittance regression.ipynb 2.1 KB
40. Part 6 Mathematics/8.1 Tensors.ipynb 2.1 KB
28. Python - Sequences/6.1 Tuples - Exercise_Py3.ipynb 2.1 KB
51. Deep Learning - Business Case Example/11. Business Case Testing the Model.srt 2.0 KB
27. Python - Python Functions/4. How to Use a Function within a Function.srt 2.0 KB
17. Statistics - Inferential Statistics Fundamentals/11. Standard error.srt 2.0 KB
51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.srt 2.0 KB
27. Python - Python Functions/3.1 Another Way to Define a Function - Solution_Py3.ipynb 2.0 KB
50. Deep Learning - Classifying on the MNIST Dataset/11. MNIST - Exercises.html 2.0 KB
18/18. Confidence intervals. Two means. Independent Samples (Part 3).srt 2.0 KB
29. Python - Iterations/6.3 Use Conditional Statements and Loops Together - Lecture_Py3.ipynb 1.9 KB
28. Python - Sequences/3.1 Help Yourself with Methods - Exercise_Py3.ipynb 1.9 KB
29. Python - Iterations/7.3 All In - Solution_Py3.ipynb 1.9 KB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.srt 1.9 KB
60. Case Study - Loading the 'absenteeism_module'/1.1 Absenteeism_new_data.csv 1.9 KB
60. Case Study - Loading the 'absenteeism_module'/1.3 scaler.original 1.9 KB
32/9.3 real_estate_price_size.csv 1.9 KB
24. Python - Basic Python Syntax/3. The Double Equality Sign.srt 1.8 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.srt 1.8 KB
39. Advanced Statistical Methods - Other Types of Clustering/3.2 Heatmaps.ipynb 1.8 KB
29. Python - Iterations/1.2 For Loops - Solution_Py3.ipynb 1.8 KB
24. Python - Basic Python Syntax/7. Add Comments.srt 1.8 KB
27. Python - Python Functions/2.1 Creating a Function with a Parameter - Solution_Py3.ipynb 1.8 KB
26. Python - Conditional Statements/3.1 Add an Else Statement - Lecture_Py3.ipynb 1.8 KB
26. Python - Conditional Statements/4.3 Else If, for Brief - Elif - Exercise_Py3.ipynb 1.7 KB
29. Python - Iterations/3.3 While Loops and Incrementing - Solution_Py3.ipynb 1.7 KB
27. Python - Python Functions/6.1 Creating Functions Containing a Few Arguments - Lecture_Py3.ipynb 1.7 KB
24. Python - Basic Python Syntax/10. Indexing Elements.srt 1.7 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.srt 1.7 KB
24. Python - Basic Python Syntax/5.1 Reassign Values - Exercise_Py3.ipynb 1.7 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5. Basic NN Example Exercises.html 1.7 KB
44. Deep Learning - TensorFlow 2.0 Introduction/5.1 TensorFlow_Minimal_example_Part1.ipynb 1.7 KB
27. Python - Python Functions/5.3 Combining Conditional Statements and Functions - Solution_Py3.ipynb 1.6 KB
32/5. Geometrical Representation of the Linear Regression Model.srt 1.6 KB
49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.srt 1.6 KB
17. Statistics - Inferential Statistics Fundamentals/1. Introduction.srt 1.6 KB
29. Python - Iterations/7.1 All In - Lecture_Py3.ipynb 1.6 KB
36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.srt 1.6 KB
25. Python - Other Python Operators/1.1 Comparison Operators - Exercise_Py3.ipynb 1.6 KB
27. Python - Python Functions/4.2 0.6.4 Using a Function in another Function - Solution_Py3.ipynb 1.6 KB
27. Python - Python Functions/2.2 Creating a Function with a Parameter - Lecture_Py3.ipynb 1.6 KB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10. Basic NN Example with TF Exercises.html 1.6 KB
36. Advanced Statistical Methods - Logistic Regression/2.1 2.01. Admittance.csv 1.6 KB
26. Python - Conditional Statements/1.2 Introduction to the If Statement - Exercise_Py3.ipynb 1.5 KB
24. Python - Basic Python Syntax/9.3 Line Continuation - Solution_Py3.ipynb 1.5 KB
24. Python - Basic Python Syntax/12.3 Structure Your Code with Indentation - Solution_Py3.ipynb 1.5 KB
32/10. Using Seaborn for Graphs.srt 1.5 KB
29. Python - Iterations/4.2 Create Lists with the range() Function - Exercise_Py3.ipynb 1.5 KB
24. Python - Basic Python Syntax/3.2 The Double Equality Sign - Lecture_Py3.ipynb 1.4 KB
26. Python - Conditional Statements/3.2 Add an Else Statement - Solution_Py3.ipynb 1.4 KB
44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.srt 1.4 KB
24. Python - Basic Python Syntax/10.1 Indexing Elements - Exercise_Py3.ipynb 1.3 KB
29. Python - Iterations/4.1 Create Lists with the range() Function - Lecture_Py3.ipynb 1.3 KB
27. Python - Python Functions/6. Functions Containing a Few Arguments.srt 1.3 KB
32/9. First Regression in Python Exercise.html 1.3 KB
24. Python - Basic Python Syntax/10.2 Indexing Elements - Lecture_Py3.ipynb 1.3 KB
10. Probability - Combinatorics/1. Fundamentals of Combinatorics.srt 1.3 KB
29. Python - Iterations/7.2 All In - Exercise_Py3.ipynb 1.3 KB
24. Python - Basic Python Syntax/5. How to Reassign Values.srt 1.3 KB
44. Deep Learning - TensorFlow 2.0 Introduction/9. Basic NN with TensorFlow Exercises.html 1.3 KB
27. Python - Python Functions/5.1 Combining Conditional Statements and Functions - Lecture_Py3.ipynb 1.3 KB
29. Python - Iterations/1.3 For Loops - Exercise_Py3.ipynb 1.3 KB
29. Python - Iterations/1.1 For Loops - Lecture_Py3.ipynb 1.3 KB
30. Python - Advanced Python Tools/3. Modules and Packages.srt 1.3 KB
27. Python - Python Functions/3.2 Another Way to Define a Function - Exercise_Py3.ipynb 1.2 KB
58. Case Study - Preprocessing the 'Absenteeism_data'/29. EXERCISE - Removing the Date Column.html 1.2 KB
33/18.1 1.03. Dummies.csv 1.2 KB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.2 Minimal_example_Part_1.ipynb 1.2 KB
27. Python - Python Functions/2.3 Creating a Function with a Parameter - Exercise_Py3.ipynb 1.2 KB
26. Python - Conditional Statements/1.3 Introduction to the If Statement - Lecture_Py3.ipynb 1.1 KB
24. Python - Basic Python Syntax/3.3 The Double Equality Sign - Solution_Py3.ipynb 1.1 KB
24. Python - Basic Python Syntax/9.2 Line Continuation - Exercise_Py3.ipynb 1.1 KB
24. Python - Basic Python Syntax/9. Understanding Line Continuation.srt 1.1 KB
29. Python - Iterations/3.2 While Loops and Incrementing - Exercise_Py3.ipynb 1.1 KB
33/3.1 1.02. Multiple linear regression.csv 1.1 KB
29. Python - Iterations/3.1 While Loops and Incrementing - Lecture_Py3.ipynb 1.1 KB
29. Python - Iterations/8.3 Iterating over Dictionaries - Lecture_Py3.ipynb 1.1 KB
34/10.2 1.02. Multiple linear regression.csv 1.1 KB
34/11.2 1.02. Multiple linear regression.csv 1.1 KB
34/12.2 1.02. Multiple linear regression.csv 1.1 KB
34/14.1 1.02. Multiple linear regression.csv 1.1 KB
34/15.1 1.02. Multiple linear regression.csv 1.1 KB
34/16.1 1.02. Multiple linear regression.csv 1.1 KB
34/7.1 1.02. Multiple linear regression.csv 1.1 KB
34/8.3 1.02. Multiple linear regression.csv 1.1 KB
34/9.1 1.02. Multiple linear regression.csv 1.1 KB
27. Python - Python Functions/5.2 Combining Conditional Statements and Functions - Exercise_Py3.ipynb 1.1 KB
52. Deep Learning - Conclusion/3. DeepMind and Deep Learning.html 1.0 KB
27. Python - Python Functions/4.3 0.6.4 Using a Function in another Function - Exercise_Py3.ipynb 1.0 KB
24. Python - Basic Python Syntax/7.1 Add Comments - Lecture_Py3.ipynb 1.0 KB
26. Python - Conditional Statements/3.3 Add an Else Statement - Exercise_Py3.ipynb 1.0 KB
60. Case Study - Loading the 'absenteeism_module'/1.2 model.original 1.0 KB
27. Python - Python Functions/4.1 0.6.4 Using a Function in another Function - Lecture_Py3.ipynb 1015 bytes
60. Case Study - Loading the 'absenteeism_module'/4. Exporting the Obtained Data Set as a .csv.html 998 bytes
60. Case Study - Loading the 'absenteeism_module'/4.1 Absenteeism Exercise - Deploying the 'absenteeism_module'.ipynb 973 bytes
24. Python - Basic Python Syntax/12.2 Structure Your Code with Indentation - Exercise_Py3.ipynb 956 bytes
32/8.3 1.01. Simple linear regression.csv 922 bytes
34/3.3 1.01. Simple linear regression.csv 922 bytes
34/4.1 1.01. Simple linear regression.csv 922 bytes
34/6.3 1.01. Simple linear regression.csv 922 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/33. A Note on Exporting Your Data as a .csv File.html 883 bytes
27. Python - Python Functions/1.1 Defining a Function in Python - Lecture_Py3.ipynb 868 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/8. EXERCISE - Dropping a Column from a DataFrame in Python.html 866 bytes
35/3. A Note on Multicollinearity.html 849 bytes
24. Python - Basic Python Syntax/3.1 The Double Equality Sign - Exercise_Py3.ipynb 838 bytes
26. Python - Conditional Statements/5.1 A Note on Boolean Values - Lecture_Py3.ipynb 791 bytes
24. Python - Basic Python Syntax/9.1 Line Continuation - Lecture_Py3.ipynb 779 bytes
34/5. A Note on Normalization.html 733 bytes
35/7. Dummy Variables - Exercise.html 713 bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/1. READ ME!!!!.html 564 bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/5. EXERCISE - Transportation Expense vs Probability.html 553 bytes
45/9. Backpropagation - A Peek into the Mathematics of Optimization.html 539 bytes
15. Statistics - Descriptive Statistics/23. Variance Exercise.html 522 bytes
60. Case Study - Loading the 'absenteeism_module'/1. Are You Sure You're All Set.html 519 bytes
35/9. Linear Regression - Exercise.html 503 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 471 bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/12. Business Case Final Exercise.html 439 bytes
51. Deep Learning - Business Case Example/12. Business Case Final Exercise.html 433 bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/3. EXERCISE - Reasons vs Probability.html 397 bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/1. EXERCISE - Age vs Probability.html 385 bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/5. Business Case Preprocessing Exercise.html 383 bytes
34/11. A Note on Calculation of P-values with sklearn.html 372 bytes
51. Deep Learning - Business Case Example/5. Business Case Preprocessing the Data - Exercise.html 370 bytes
36. Advanced Statistical Methods - Logistic Regression/15.1 2.03. Test dataset.csv 322 bytes
59/15. EXERCISE - Saving the Model (and Scaler).html 284 bytes
38. Advanced Statistical Methods - K-Means Clustering/11.1 3.12. Example.csv 283 bytes
39. Advanced Statistical Methods - Other Types of Clustering/3.3 Country clusters standardized.csv 244 bytes
59/11.1 Logistic Regression prior to Backward Elimination.html 226 bytes
59/9.1 Logistic Regression prior to Custom Scaler.html 219 bytes
59/15.1 Logistic Regression with Comments.html 210 bytes
38. Advanced Statistical Methods - K-Means Clustering/2.3 3.01. Country clusters.csv 200 bytes
59/15.2 Logistic Regression.html 196 bytes
51. Deep Learning - Business Case Example/10. Setting an Early Stopping Mechanism - Exercise.html 192 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/18. EXERCISE - Using .concat() in Python.html 189 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167 bytes
10. Probability - Combinatorics/10. Solving Variations without Repetition.html 165 bytes
10. Probability - Combinatorics/12. Solving Combinations.html 165 bytes
10. Probability - Combinatorics/14. Symmetry of Combinations.html 165 bytes
10. Probability - Combinatorics/16. Solving Combinations with Separate Sample Spaces.html 165 bytes
10. Probability - Combinatorics/18. Combinatorics in Real-Life The Lottery.html 165 bytes
10. Probability - Combinatorics/2. Fundamentals of Combinatorics.html 165 bytes
10. Probability - Combinatorics/4. Permutations and How to Use Them.html 165 bytes
10. Probability - Combinatorics/6. Simple Operations with Factorials.html 165 bytes
10. Probability - Combinatorics/8. Solving Variations with Repetition.html 165 bytes
11. Probability - Bayesian Inference/10. Mutually Exclusive Sets.html 165 bytes
11. Probability - Bayesian Inference/12. Dependence and Independence of Sets.html 165 bytes
11. Probability - Bayesian Inference/14. The Conditional Probability Formula.html 165 bytes
11. Probability - Bayesian Inference/17. The Additive Rule.html 165 bytes
11. Probability - Bayesian Inference/19. The Multiplication Law.html 165 bytes
11. Probability - Bayesian Inference/2. Sets and Events.html 165 bytes
11. Probability - Bayesian Inference/21. Bayes' Law.html 165 bytes
11. Probability - Bayesian Inference/4. Ways Sets Can Interact.html 165 bytes
11. Probability - Bayesian Inference/6. Intersection of Sets.html 165 bytes
11. Probability - Bayesian Inference/8. Union of Sets.html 165 bytes
12. Probability - Distributions/10. Discrete Distributions The Bernoulli Distribution.html 165 bytes
12. Probability - Distributions/12. Discrete Distributions The Binomial Distribution.html 165 bytes
12. Probability - Distributions/14. Discrete Distributions The Poisson Distribution.html 165 bytes
12. Probability - Distributions/16. Characteristics of Continuous Distributions.html 165 bytes
12. Probability - Distributions/18. Continuous Distributions The Normal Distribution.html 165 bytes
12. Probability - Distributions/2. Fundamentals of Probability Distributions.html 165 bytes
12. Probability - Distributions/20. Continuous Distributions The Standard Normal Distribution.html 165 bytes
12. Probability - Distributions/22. Continuous Distributions The Students' T Distribution.html 165 bytes
12. Probability - Distributions/24. Continuous Distributions The Chi-Squared Distribution.html 165 bytes
12. Probability - Distributions/26. Continuous Distributions The Exponential Distribution.html 165 bytes
12. Probability - Distributions/28. Continuous Distributions The Logistic Distribution.html 165 bytes
12. Probability - Distributions/4. Types of Probability Distributions.html 165 bytes
12. Probability - Distributions/6. Characteristics of Discrete Distributions.html 165 bytes
12. Probability - Distributions/8. Discrete Distributions The Uniform Distribution.html 165 bytes
14. Part 3 Statistics/2. Population and Sample.html 165 bytes
15. Statistics - Descriptive Statistics/12. The Histogram.html 165 bytes
15. Statistics - Descriptive Statistics/15. Cross Tables and Scatter Plots.html 165 bytes
15. Statistics - Descriptive Statistics/2. Types of Data.html 165 bytes
15. Statistics - Descriptive Statistics/20. Skewness.html 165 bytes
15. Statistics - Descriptive Statistics/25. Standard Deviation.html 165 bytes
15. Statistics - Descriptive Statistics/28. Covariance.html 165 bytes
15. Statistics - Descriptive Statistics/31. Correlation.html 165 bytes
15. Statistics - Descriptive Statistics/4. Levels of Measurement.html 165 bytes
15. Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html 165 bytes
15. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html 165 bytes
17. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html 165 bytes
18/11. Margin of Error.html 165 bytes
18/2. What are Confidence Intervals.html 165 bytes
18/7. Student's T Distribution.html 165 bytes
2/10. A Breakdown of our Data Science Infographic.html 165 bytes
2/2. Data Science and Business Buzzwords Why are there so Many.html 165 bytes
2/4. What is the difference between Analysis and Analytics.html 165 bytes
2/6. Business Analytics, Data Analytics, and Data Science An Introduction.html 165 bytes
2/8. Continuing with BI, ML, and AI.html 165 bytes
20. Statistics - Hypothesis Testing/11. p-value.html 165 bytes
20. Statistics - Hypothesis Testing/19. Test for the mean. Independent Samples (Part 2).html 165 bytes
20. Statistics - Hypothesis Testing/3. Null vs Alternative Hypothesis.html 165 bytes
20. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html 165 bytes
20. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html 165 bytes
22. Part 4 Introduction to Python/10. Jupyter's Interface.html 165 bytes
22. Part 4 Introduction to Python/2. Introduction to Programming.html 165 bytes
22. Part 4 Introduction to Python/4. Why Python.html 165 bytes
22. Part 4 Introduction to Python/6. Why Jupyter.html 165 bytes
23. Python - Variables and Data Types/2. Variables.html 165 bytes
23. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html 165 bytes
23. Python - Variables and Data Types/6. Python Strings.html 165 bytes
24. Python - Basic Python Syntax/11. Indexing Elements.html 165 bytes
24. Python - Basic Python Syntax/13. Structuring with Indentation.html 165 bytes
24. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html 165 bytes
24. Python - Basic Python Syntax/4. The Double Equality Sign.html 165 bytes
24. Python - Basic Python Syntax/6. How to Reassign Values.html 165 bytes
24. Python - Basic Python Syntax/8. Add Comments.html 165 bytes
25. Python - Other Python Operators/2. Comparison Operators.html 165 bytes
25. Python - Other Python Operators/4. Logical and Identity Operators.html 165 bytes
26. Python - Conditional Statements/2. The IF Statement.html 165 bytes
26. Python - Conditional Statements/6. A Note on Boolean Values.html 165 bytes
27. Python - Python Functions/8. Python Functions.html 165 bytes
28. Python - Sequences/2. Lists.html 165 bytes
28. Python - Sequences/4. Using Methods.html 165 bytes
28. Python - Sequences/8. Dictionaries.html 165 bytes
29. Python - Iterations/2. For Loops.html 165 bytes
29. Python - Iterations/5. Lists with the range() Function.html 165 bytes
3/2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html 165 bytes
30. Python - Advanced Python Tools/2. Object Oriented Programming.html 165 bytes
30. Python - Advanced Python Tools/4. Modules and Packages.html 165 bytes
30. Python - Advanced Python Tools/6. What is the Standard Library.html 165 bytes
30. Python - Advanced Python Tools/8. Importing Modules in Python.html 165 bytes
31. Part 5 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html 165 bytes
32/12. How to Interpret the Regression Table.html 165 bytes
32/14. Decomposition of Variability.html 165 bytes
32/16. What is the OLS.html 165 bytes
32/18. R-Squared.html 165 bytes
32/2. The Linear Regression Model.html 165 bytes
32/4. Correlation vs Regression.html 165 bytes
32/6. Geometrical Representation of the Linear Regression Model.html 165 bytes
33/10. A1 Linearity.html 165 bytes
33/12. A2 No Endogeneity.html 165 bytes
33/15. A4 No autocorrelation.html 165 bytes
33/17. A5 No Multicollinearity.html 165 bytes
33/2. Multiple Linear Regression.html 165 bytes
33/4. Adjusted R-Squared.html 165 bytes
33/8. OLS Assumptions.html 165 bytes
4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason Behind These Disciplines.html 165 bytes
40. Part 6 Mathematics/11. Addition and Subtraction of Matrices.html 165 bytes
40. Part 6 Mathematics/2. What is a Matrix.html 165 bytes
40. Part 6 Mathematics/4. Scalars and Vectors.html 165 bytes
40. Part 6 Mathematics/6. Linear Algebra and Geometry.html 165 bytes
40. Part 6 Mathematics/9. What is a Tensor.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/4. Training the Model.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html 165 bytes
42. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/5. Techniques for Working with Big Data.html 165 bytes
5. The Field of Data Science - Popular Data Science Techniques/8. Business Intelligence (BI) Techniques.html 165 bytes
56. Software Integration/10. Software Integration - Explained.html 165 bytes
56. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html 165 bytes
56. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html 165 bytes
56. Software Integration/6. Taking a Closer Look at APIs.html 165 bytes
56. Software Integration/8. Communication between Software Products through Text Files.html 165 bytes
57. Case Study - What's Next in the Course/4. Introducing the Data Set.html 165 bytes
6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html 165 bytes
7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html 165 bytes
8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html 165 bytes
9. Part 2 Probability/2. The Basic Probability Formula.html 165 bytes
9. Part 2 Probability/4. Computing Expected Values.html 165 bytes
9. Part 2 Probability/6. Frequency.html 165 bytes
9. Part 2 Probability/8. Events and Their Complements.html 165 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html 142 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 bytes
1. Part 1 Introduction/3.1 Download all resources.html 134 bytes
35/4.3 sklearn - Linear Regression - Practical Example (Part 3).html 134 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 bytes
[Tutorialsplanet.NET].url 128 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 117 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html 116 bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/9. SOLUTION - Dropping a Column from a DataFrame in Python.html 113 bytes
36. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html 87 bytes
36. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html 87 bytes
36. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html 87 bytes
36. Advanced Statistical Methods - Logistic Regression/5. Building a Logistic Regression - Exercise.html 87 bytes
36. Advanced Statistical Methods - Logistic Regression/8. Understanding Logistic Regression Tables - Exercise.html 87 bytes
38. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 bytes
38. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 bytes
38. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html 87 bytes
38. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data - Exercise.html 87 bytes
38. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html 87 bytes
15. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/13. Histogram Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/21. Skewness Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/29. Covariance Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html 81 bytes
15. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html 81 bytes
16. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html 81 bytes
17. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html 81 bytes
18/13. Confidence intervals. Two means. Dependent samples Exercise.html 81 bytes
18/15. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 bytes
18/17. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 bytes
18/4. Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 bytes
18/9. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 bytes
19. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html 81 bytes
20. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html 81 bytes
20. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html 81 bytes
20. Statistics - Hypothesis Testing/17. Test for the mean. Independent Samples (Part 1). Exercise.html 81 bytes
20. Statistics - Hypothesis Testing/20. Test for the mean. Independent Samples (Part 2). Exercise.html 81 bytes
20. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html 81 bytes
21. Statistics - Practical Example Hypothesis Testing/2. Practical Example Hypothesis Testing Exercise.html 81 bytes
50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html 79 bytes
50. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html 79 bytes
51. Deep Learning - Business Case Example/7. Business Case Load the Preprocessed Data - Exercise.html 79 bytes
33/19. Dealing with Categorical Data - Dummy Variables.html 76 bytes
33/5. Multiple Linear Regression Exercise.html 76 bytes
34/13. Multiple Linear Regression - Exercise.html 76 bytes
34/17. Feature Scaling (Standardization) - Exercise.html 76 bytes
34/6. Simple Linear Regression with sklearn - Exercise.html 76 bytes
34/9. Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 bytes
35/5. Dummies and Variance Inflation Factor - Exercise.html 76 bytes

[Tutorialsplanet.NET] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

File List

Download Torrent

Related Resources

Copyright Infringement