[GigaCourse.Com] Udemy - The Data Science Course Complete Data Science Bootcamp 2023

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11 - Probability Bayesian Inference/51 - A Practical Example of Bayesian Inference.mp4 299.0 MB
12 - Probability Distributions/66 - A Practical Example of Probability Distributions.mp4 283.7 MB
16 - Statistics Practical Example Descriptive Statistics/93 - Practical Example Descriptive Statistics.mp4 247.3 MB
35 - Advanced Statistical Methods Practical Example Linear Regression/224 - Practical Example Linear Regression Part 1.mp4 176.1 MB
5 - The Field of Data Science Popular Data Science Techniques/11 - Techniques for Working with Traditional Data.mp4 165.5 MB
64 - Appendix Working with Text Files in Python/505 - Importing Data from json Files.mp4 159.7 MB
58 - Case Study Preprocessing the Absenteeismdata/420 - Obtaining Dummies from a Single Feature.mp4 151.7 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - Business Case Preprocessing.mp4 146.3 MB
51 - Deep Learning Business Case Example/354 - Business Case Preprocessing the Data.mp4 145.2 MB
3 - The Field of Data Science Connecting the Data Science Disciplines/9 - Applying Traditional Data Big Data BI Traditional Data Science and ML.mp4 134.7 MB
19 - Statistics Practical Example Inferential Statistics/118 - Practical Example Inferential Statistics.mp4 134.0 MB
6 - The Field of Data Science Popular Data Science Tools/22 - Necessary Programming Languages and Software Used in Data Science.mp4 132.1 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Business Case Getting Acquainted with the Dataset.mp4 124.6 MB
58 - Case Study Preprocessing the Absenteeismdata/425 - Classifying the Various Reasons for Absence.mp4 122.7 MB
10 - Probability Combinatorics/39 - A Practical Example of Combinatorics.mp4 120.8 MB
58 - Case Study Preprocessing the Absenteeismdata/412 - Checking the Content of the Data Set.mp4 116.1 MB
40 - Part 6 Mathematics/281 - Why is Linear Algebra Useful.mp4 113.4 MB
5 - The Field of Data Science Popular Data Science Techniques/17 - Techniques for Working with Traditional Methods.mp4 112.7 MB
64 - Appendix Working with Text Files in Python/502 - Importing Data with loadtxt and genfromtxt.mp4 110.9 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/395 - Creating a Data Provider.mp4 110.3 MB
5 - The Field of Data Science Popular Data Science Techniques/20 - Types of Machine Learning.mp4 108.9 MB
64 - Appendix Working with Text Files in Python/498 - Importing csv Files Part I.mp4 104.3 MB
35 - Advanced Statistical Methods Practical Example Linear Regression/231 - Practical Example Linear Regression Part 5.mp4 103.0 MB
51 - Deep Learning Business Case Example/351 - Business Case Exploring the Dataset and Identifying Predictors.mp4 101.1 MB
18 - Statistics Inferential Statistics Confidence Intervals/104 - Confidence Intervals Population Variance Known Zscore.mp4 100.9 MB
5 - The Field of Data Science Popular Data Science Techniques/13 - Techniques for Working with Big Data.mp4 100.9 MB
60 - Case Study Loading the absenteeismmodule/461 - Deploying the absenteeismmodule Part II.mp4 100.7 MB
56 - Software Integration/404 - Taking a Closer Look at APIs.mp4 97.5 MB
8 - The Field of Data Science Debunking Common Misconceptions/24 - Debunking Common Misconceptions.mp4 96.2 MB
18 - Statistics Inferential Statistics Confidence Intervals/111 - Confidence intervals Two means Dependent samples.mp4 92.2 MB
4 - The Field of Data Science The Benefits of Each Discipline/10 - The Reason Behind These Disciplines.mp4 90.9 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - Business Case Model Outline.mp4 89.4 MB
64 - Appendix Working with Text Files in Python/500 - Importing csv Files Part III.mp4 88.7 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - MNIST Results and Testing.mp4 88.5 MB
64 - Appendix Working with Text Files in Python/506 - An Introduction to Working with Excel Files in Python.mp4 88.1 MB
64 - Appendix Working with Text Files in Python/508 - Importing Data in Python an Important Exercise.mp4 87.8 MB
56 - Software Integration/403 - What are Data Connectivity APIs and Endpoints.mp4 87.2 MB
64 - Appendix Working with Text Files in Python/503 - Importing Data Partial Cleaning While Importing Data.mp4 86.3 MB
21 - Statistics Practical Example Hypothesis Testing/135 - Practical Example Hypothesis Testing.mp4 84.9 MB
51 - Deep Learning Business Case Example/359 - Business Case Setting an Early Stopping Mechanism.mp4 84.9 MB
58 - Case Study Preprocessing the Absenteeismdata/416 - Dropping a Column from a DataFrame in Python.mp4 80.7 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/450 - Interpreting the Coefficients for Our Problem.mp4 80.3 MB
61 - Case Study Analyzing the Predicted Outputs in Tableau/466 - Analyzing Reasons vs Probability in Tableau.mp4 80.1 MB
58 - Case Study Preprocessing the Absenteeismdata/436 - Extracting the Month Value from the Date Column.mp4 77.4 MB
61 - Case Study Analyzing the Predicted Outputs in Tableau/464 - Analyzing Age vs Probability in Tableau.mp4 77.0 MB
35 - Advanced Statistical Methods Practical Example Linear Regression/229 - Practical Example Linear Regression Part 4.mp4 75.5 MB
2 - The Field of Data Science The Various Data Science Disciplines/8 - A Breakdown of our Data Science Infographic.mp4 74.4 MB
63 - Appendix pandas Fundamentals/485 - Data Selection in pandas DataFrames.mp4 73.6 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/448 - Fitting the Model and Assessing its Accuracy.mp4 73.1 MB
40 - Part 6 Mathematics/280 - Dot Product of Matrices.mp4 72.6 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/447 - Splitting the Data for Training and Testing.mp4 69.9 MB
5 - The Field of Data Science Popular Data Science Techniques/15 - Business Intelligence BI Techniques.mp4 69.7 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dealing with Categorical Data Dummy Variables.mp4 69.3 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/223 - Train Test Split Explained.mp4 68.1 MB
1 - Part 1 Introduction/1 - A Practical Example What You Will Learn in This Course.mp4 67.8 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Adjusted RSquared.mp4 67.2 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/451 - Standardizing only the Numerical Variables Creating a Custom Scaler.mp4 66.8 MB
20 - Statistics Hypothesis Testing/129 - Test for the Mean Dependent Samples.mp4 66.3 MB
9 - Part 2 Probability/26 - Computing Expected Values.mp4 65.9 MB
5 - The Field of Data Science Popular Data Science Techniques/19 - Machine Learning ML Techniques.mp4 65.8 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - MNIST Model Outline.mp4 65.7 MB
38 - Advanced Statistical Methods KMeans Clustering/254 - A Simple Example of Clustering.mp4 65.4 MB
22 - Part 4 Introduction to Python/140 - Installing Python and Jupyter.mp4 64.7 MB
15 - Statistics Descriptive Statistics/71 - Types of Data.mp4 64.6 MB
62 - Appendix Additional Python Tools/472 - Triple Nested For Loops.mp4 64.0 MB
38 - Advanced Statistical Methods KMeans Clustering/264 - Market Segmentation with Cluster Analysis Part 2.mp4 63.6 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/444 - Creating the Targets for the Logistic Regression.mp4 63.5 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Backward Elimination or How to Simplify Your Model.mp4 63.3 MB
28 - Python Sequences/169 - Dictionaries.mp4 63.2 MB
13 - Probability Probability in Other Fields/67 - Probability in Finance.mp4 62.5 MB
56 - Software Integration/406 - Software Integration Explained.mp4 62.5 MB
63 - Appendix pandas Fundamentals/486 - pandas DataFrames Indexing with iloc.mp4 62.1 MB
35 - Advanced Statistical Methods Practical Example Linear Regression/225 - Practical Example Linear Regression Part 2.mp4 61.9 MB
51 - Deep Learning Business Case Example/358 - Business Case Learning and Interpreting the Result.mp4 61.4 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/454 - Testing the Model We Created.mp4 61.3 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - MNIST Learning.mp4 60.0 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/207 - Simple Linear Regression with sklearn.mp4 59.7 MB
20 - Statistics Hypothesis Testing/122 - Rejection Region and Significance Level.mp4 59.4 MB
50 - Deep Learning Classifying on the MNIST Dataset/348 - MNIST Learning.mp4 59.1 MB
7 - The Field of Data Science Careers in Data Science/23 - Finding the Job What to Expect and What to Look for.mp4 58.5 MB
63 - Appendix pandas Fundamentals/480 - Using unique and nunique.mp4 57.1 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - First Regression in Python.mp4 56.0 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/199 - A3 Normality and Homoscedasticity.mp4 55.6 MB
58 - Case Study Preprocessing the Absenteeismdata/426 - Using concat in Python.mp4 55.0 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/458 - Preparing the Deployment of the Model through a Module.mp4 54.9 MB
2 - The Field of Data Science The Various Data Science Disciplines/7 - Continuing with BI ML and AI.mp4 54.8 MB
12 - Probability Distributions/59 - Characteristics of Continuous Distributions.mp4 54.7 MB
38 - Advanced Statistical Methods KMeans Clustering/258 - How to Choose the Number of Clusters.mp4 54.5 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/188 - How to Interpret the Regression Table.mp4 54.4 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/219 - Feature Selection through Standardization of Weights.mp4 53.9 MB
58 - Case Study Preprocessing the Absenteeismdata/419 - Analyzing the Reasons for Absence.mp4 53.9 MB
44 - Deep Learning TensorFlow 20 Introduction/300 - How to Install TensorFlow 20.mp4 53.7 MB
38 - Advanced Statistical Methods KMeans Clustering/263 - Market Segmentation with Cluster Analysis Part 1.mp4 53.6 MB
17 - Statistics Inferential Statistics Fundamentals/97 - The Normal Distribution.mp4 53.6 MB
15 - Statistics Descriptive Statistics/73 - Categorical Variables Visualization Techniques.mp4 53.2 MB
14 - Part 3 Statistics/70 - Population and Sample.mp4 53.0 MB
9 - Part 2 Probability/27 - Frequency.mp4 52.6 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/190 - What is the OLS.mp4 52.3 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/449 - Creating a Summary Table with the Coefficients and Intercept.mp4 52.3 MB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Basic NN Example Part 4.mp4 52.0 MB
1 - Part 1 Introduction/2 - What Does the Course Cover.mp4 51.9 MB
20 - Statistics Hypothesis Testing/126 - pvalue.mp4 51.7 MB
64 - Appendix Working with Text Files in Python/497 - Importing Text Files with open.mp4 51.4 MB
44 - Deep Learning TensorFlow 20 Introduction/305 - Outlining the Model with TensorFlow 2.mp4 51.2 MB
38 - Advanced Statistical Methods KMeans Clustering/265 - How is Clustering Useful.mp4 50.9 MB
12 - Probability Distributions/53 - Types of Probability Distributions.mp4 50.9 MB
63 - Appendix pandas Fundamentals/484 - pandas DataFrames Common Attributes.mp4 50.8 MB
20 - Statistics Hypothesis Testing/120 - Null vs Alternative Hypothesis.mp4 50.7 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - Business Case Optimization.mp4 50.6 MB
64 - Appendix Working with Text Files in Python/512 - Saving Your Data with NumPy Part I npy.mp4 50.2 MB
44 - Deep Learning TensorFlow 20 Introduction/306 - Interpreting the Result and Extracting the Weights and Bias.mp4 50.2 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/455 - Saving the Model and Preparing it for Deployment.mp4 50.1 MB
64 - Appendix Working with Text Files in Python/496 - Importing Text Files open.mp4 49.8 MB
40 - Part 6 Mathematics/276 - Addition and Subtraction of Matrices.mp4 49.7 MB
62 - Appendix Additional Python Tools/473 - List Comprehensions.mp4 49.3 MB
37 - Advanced Statistical Methods Cluster Analysis/250 - Some Examples of Clusters.mp4 48.9 MB
18 - Statistics Inferential Statistics Confidence Intervals/110 - Margin of Error.mp4 48.5 MB
13 - Probability Probability in Other Fields/68 - Probability in Statistics.mp4 48.5 MB
52 - Deep Learning Conclusion/366 - An overview of CNNs.mp4 48.3 MB
15 - Statistics Descriptive Statistics/81 - Mean median and mode.mp4 48.0 MB
20 - Statistics Hypothesis Testing/133 - Test for the mean Independent Samples Part 2.mp4 46.9 MB
64 - Appendix Working with Text Files in Python/509 - Importing Data with the squeeze Method.mp4 46.1 MB
9 - Part 2 Probability/25 - The Basic Probability Formula.mp4 46.0 MB
62 - Appendix Additional Python Tools/469 - Using the format Method.mp4 45.4 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/184 - Python Packages Installation.mp4 45.4 MB
15 - Statistics Descriptive Statistics/72 - Levels of Measurement.mp4 45.0 MB
12 - Probability Distributions/57 - Discrete Distributions The Binomial Distribution.mp4 44.5 MB
15 - Statistics Descriptive Statistics/85 - Variance.mp4 44.1 MB
50 - Deep Learning Classifying on the MNIST Dataset/342 - MNIST Preprocess the Data Create a Validation Set and Scale It.mp4 43.7 MB
18 - Statistics Inferential Statistics Confidence Intervals/103 - What are Confidence Intervals.mp4 43.6 MB
50 - Deep Learning Classifying on the MNIST Dataset/350 - MNIST Testing the Model.mp4 43.4 MB
5 - The Field of Data Science Popular Data Science Techniques/18 - Real Life Examples of Traditional Methods.mp4 42.7 MB
36 - Advanced Statistical Methods Logistic Regression/234 - A Simple Example in Python.mp4 42.6 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/311 - Digging into a Deep Net.mp4 42.4 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/208 - Simple Linear Regression with sklearn A StatsModelslike Summary Table.mp4 42.3 MB
40 - Part 6 Mathematics/278 - Transpose of a Matrix.mp4 42.3 MB
17 - Statistics Inferential Statistics Fundamentals/102 - Estimators and Estimates.mp4 42.2 MB
62 - Appendix Additional Python Tools/474 - Anonymous Lambda Functions.mp4 41.5 MB
36 - Advanced Statistical Methods Logistic Regression/235 - Logistic vs Logit Function.mp4 41.4 MB
50 - Deep Learning Classifying on the MNIST Dataset/346 - MNIST Outline the Model.mp4 41.3 MB
58 - Case Study Preprocessing the Absenteeismdata/411 - Importing the Absenteeism Data in Python.mp4 41.3 MB
36 - Advanced Statistical Methods Logistic Regression/247 - Testing the Model.mp4 41.2 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/220 - Predicting with the Standardized Coefficients.mp4 41.1 MB
28 - Python Sequences/166 - Lists.mp4 41.0 MB
63 - Appendix pandas Fundamentals/487 - pandas DataFrames Indexing with loc.mp4 41.0 MB
58 - Case Study Preprocessing the Absenteeismdata/441 - Final Remarks of this Section.mp4 41.0 MB
63 - Appendix pandas Fundamentals/479 - Parameters and Arguments in pandas.mp4 40.9 MB
5 - The Field of Data Science Popular Data Science Techniques/16 - Real Life Examples of Business Intelligence BI.mp4 40.8 MB
64 - Appendix Working with Text Files in Python/514 - Saving Your Data with NumPy Part III csv.mp4 40.8 MB
64 - Appendix Working with Text Files in Python/511 - Saving Your Data with pandas.mp4 40.7 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/212 - Calculating the Adjusted RSquared in sklearn.mp4 40.7 MB
15 - Statistics Descriptive Statistics/91 - Correlation Coefficient.mp4 40.0 MB
60 - Case Study Loading the absenteeismmodule/460 - Deploying the absenteeismmodule Part I.mp4 39.3 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/214 - Feature Selection Fregression.mp4 39.2 MB
13 - Probability Probability in Other Fields/69 - Probability in Data Science.mp4 38.9 MB
29 - Python Iterations/171 - While Loops and Incrementing.mp4 38.8 MB
23 - Python Variables and Data Types/145 - Python Strings.mp4 38.5 MB
63 - Appendix pandas Fundamentals/475 - Introduction to pandas Series.mp4 38.5 MB
64 - Appendix Working with Text Files in Python/510 - Importing Files in Jupyter.mp4 38.4 MB
29 - Python Iterations/172 - Lists with the range Function.mp4 38.4 MB
58 - Case Study Preprocessing the Absenteeismdata/437 - Extracting the Day of the Week from the Date Column.mp4 38.1 MB
36 - Advanced Statistical Methods Logistic Regression/244 - Calculating the Accuracy of the Model.mp4 37.9 MB
36 - Advanced Statistical Methods Logistic Regression/238 - An Invaluable Coding Tip.mp4 37.8 MB
25 - Python Other Python Operators/154 - Logical and Identity Operators.mp4 37.4 MB
40 - Part 6 Mathematics/274 - Arrays in Python A Convenient Way To Represent Matrices.mp4 37.3 MB
50 - Deep Learning Classifying on the MNIST Dataset/344 - MNIST Preprocess the Data Shuffle and Batch.mp4 37.2 MB
22 - Part 4 Introduction to Python/142 - Prerequisites for Coding in the Jupyter Notebooks.mp4 37.0 MB
15 - Statistics Descriptive Statistics/87 - Standard Deviation and Coefficient of Variation.mp4 36.8 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/312 - NonLinearities and their Purpose.mp4 36.8 MB
9 - Part 2 Probability/28 - Events and Their Complements.mp4 36.5 MB
51 - Deep Learning Business Case Example/353 - Business Case Balancing the Dataset.mp4 36.4 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - The Importance of Working with a Balanced Dataset.mp4 36.3 MB
20 - Statistics Hypothesis Testing/127 - Test for the Mean Population Variance Unknown.mp4 36.1 MB
17 - Statistics Inferential Statistics Fundamentals/100 - Central Limit Theorem.mp4 36.0 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/398 - Business Case Interpretation.mp4 35.8 MB
15 - Statistics Descriptive Statistics/89 - Covariance.mp4 35.7 MB
58 - Case Study Preprocessing the Absenteeismdata/440 - Working on Education Children and Pets.mp4 35.7 MB
15 - Statistics Descriptive Statistics/79 - Cross Tables and Scatter Plots.mp4 35.5 MB
11 - Probability Bayesian Inference/43 - Union of Sets.mp4 35.2 MB
12 - Probability Distributions/58 - Discrete Distributions The Poisson Distribution.mp4 35.1 MB
10 - Probability Combinatorics/34 - Solving Combinations.mp4 35.0 MB
61 - Case Study Analyzing the Predicted Outputs in Tableau/468 - Analyzing Transportation Expense vs Probability in Tableau.mp4 35.0 MB
63 - Appendix pandas Fundamentals/477 - Working with Methods in Python Part I.mp4 34.8 MB
57 - Case Study Whats Next in the Course/409 - Introducing the Data Set.mp4 34.8 MB
58 - Case Study Preprocessing the Absenteeismdata/435 - Analyzing the Dates from the Initial Data Set.mp4 34.7 MB
56 - Software Integration/405 - Communication between Software Products through Text Files.mp4 34.4 MB
58 - Case Study Preprocessing the Absenteeismdata/413 - Introduction to Terms with Multiple Meanings.mp4 34.2 MB
39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps.mp4 34.2 MB
58 - Case Study Preprocessing the Absenteeismdata/432 - Creating Checkpoints while Coding in Jupyter.mp4 34.2 MB
15 - Statistics Descriptive Statistics/75 - Numerical Variables Frequency Distribution Table.mp4 34.1 MB
40 - Part 6 Mathematics/275 - What is a Tensor.mp4 33.5 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - Calculating the Accuracy of the Model.mp4 32.6 MB
64 - Appendix Working with Text Files in Python/513 - Saving Your Data with NumPy Part II npz.mp4 32.5 MB
42 - Deep Learning Introduction to Neural Networks/293 - Optimization Algorithm 1Parameter Gradient Descent.mp4 32.4 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Business Case A Comment on the Homework.mp4 32.2 MB
36 - Advanced Statistical Methods Logistic Regression/242 - Binary Predictors in a Logistic Regression.mp4 32.1 MB
28 - Python Sequences/168 - Tuples.mp4 32.0 MB
11 - Probability Bayesian Inference/50 - Bayes Law.mp4 32.0 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/218 - Feature Scaling Standardization.mp4 31.9 MB
44 - Deep Learning TensorFlow 20 Introduction/307 - Customizing a TensorFlow 2 Model.mp4 31.6 MB
56 - Software Integration/402 - What are Data Servers Clients Requests and Responses.mp4 31.4 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making Predictions with the Linear Regression.mp4 31.2 MB
63 - Appendix pandas Fundamentals/483 - Introduction to pandas DataFrames Part II.mp4 30.7 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/383 - MNIST Loss and Optimization Algorithm.mp4 30.2 MB
12 - Probability Distributions/52 - Fundamentals of Probability Distributions.mp4 30.1 MB
12 - Probability Distributions/60 - Continuous Distributions The Normal Distribution.mp4 30.0 MB
20 - Statistics Hypothesis Testing/124 - Test for the Mean Population Variance Known.mp4 29.9 MB
12 - Probability Distributions/61 - Continuous Distributions The Standard Normal Distribution.mp4 29.8 MB
57 - Case Study Whats Next in the Course/407 - Game Plan for this Python SQL and Tableau Business Exercise.mp4 29.7 MB
11 - Probability Bayesian Inference/41 - Ways Sets Can Interact.mp4 29.6 MB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Basic NN Example Part 3.mp4 29.4 MB
2 - The Field of Data Science The Various Data Science Disciplines/6 - Business Analytics Data Analytics and Data Science An Introduction.mp4 29.4 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - Basic NN Example with TF Inputs Outputs Targets Weights Biases.mp4 29.1 MB
64 - Appendix Working with Text Files in Python/507 - Working with Excel xlsx Data.mp4 28.9 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/446 - Standardizing the Data.mp4 28.6 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - Basic NN Example with TF Model Output.mp4 28.2 MB
20 - Statistics Hypothesis Testing/131 - Test for the mean Independent Samples Part 1.mp4 28.2 MB
29 - Python Iterations/175 - How to Iterate over Dictionaries.mp4 27.7 MB
11 - Probability Bayesian Inference/46 - The Conditional Probability Formula.mp4 27.5 MB
63 - Appendix pandas Fundamentals/481 - Using sortvalues.mp4 27.3 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - Basic NN Example with TF Loss Function and Gradient Descent.mp4 27.3 MB
11 - Probability Bayesian Inference/40 - Sets and Events.mp4 27.2 MB
28 - Python Sequences/167 - List Slicing.mp4 27.1 MB
18 - Statistics Inferential Statistics Confidence Intervals/106 - Confidence Interval Clarifications.mp4 27.0 MB
17 - Statistics Inferential Statistics Fundamentals/96 - What is a Distribution.mp4 26.9 MB
5 - The Field of Data Science Popular Data Science Techniques/21 - Real Life Examples of Machine Learning ML.mp4 26.8 MB
15 - Statistics Descriptive Statistics/83 - Skewness.mp4 26.7 MB
10 - Probability Combinatorics/30 - Permutations and How to Use Them.mp4 26.5 MB
5 - The Field of Data Science Popular Data Science Techniques/12 - Real Life Examples of Traditional Data.mp4 26.4 MB
58 - Case Study Preprocessing the Absenteeismdata/439 - Analyzing Several Straightforward Columns for this Exercise.mp4 26.2 MB
29 - Python Iterations/173 - Conditional Statements and Loops.mp4 26.0 MB
10 - Probability Combinatorics/37 - Combinatorics in RealLife The Lottery.mp4 25.6 MB
26 - Python Conditional Statements/157 - The ELIF Statement.mp4 25.6 MB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/296 - Basic NN Example Part 2.mp4 25.4 MB
20 - Statistics Hypothesis Testing/123 - Type I Error and Type II Error.mp4 25.4 MB
11 - Probability Bayesian Inference/49 - The Multiplication Law.mp4 25.1 MB
12 - Probability Distributions/65 - Continuous Distributions The Logistic Distribution.mp4 25.0 MB
18 - Statistics Inferential Statistics Confidence Intervals/115 - Confidence intervals Two means Independent Samples Part 2.mp4 25.0 MB
64 - Appendix Working with Text Files in Python/492 - Importing Data in Python Principles.mp4 25.0 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/330 - Learning Rate Schedules or How to Choose the Optimal Learning Rate.mp4 25.0 MB
40 - Part 6 Mathematics/279 - Dot Product.mp4 24.9 MB
12 - Probability Distributions/64 - Continuous Distributions The Exponential Distribution.mp4 24.4 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/452 - Interpreting the Coefficients of the Logistic Regression.mp4 24.3 MB
39 - Advanced Statistical Methods Other Types of Clustering/269 - Dendrogram.mp4 24.3 MB
18 - Statistics Inferential Statistics Confidence Intervals/108 - Confidence Intervals Population Variance Unknown Tscore.mp4 24.2 MB
36 - Advanced Statistical Methods Logistic Regression/239 - Understanding Logistic Regression Tables.mp4 24.2 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/372 - TensorFlow Intro.mp4 23.7 MB
2 - The Field of Data Science The Various Data Science Disciplines/4 - Data Science and Business Buzzwords Why are there so Many.mp4 23.7 MB
10 - Probability Combinatorics/38 - A Recap of Combinatorics.mp4 23.4 MB
50 - Deep Learning Classifying on the MNIST Dataset/341 - MNIST Importing the Relevant Packages and Loading the Data.mp4 23.0 MB
46 - Deep Learning Overfitting/319 - Underfitting and Overfitting for Classification.mp4 22.9 MB
64 - Appendix Working with Text Files in Python/499 - Importing csv Files Part II.mp4 22.9 MB
22 - Part 4 Introduction to Python/137 - Introduction to Programming.mp4 22.6 MB
62 - Appendix Additional Python Tools/470 - Iterating Over Range Objects.mp4 22.6 MB
42 - Deep Learning Introduction to Neural Networks/294 - Optimization Algorithm nParameter Gradient Descent.mp4 22.3 MB
42 - Deep Learning Introduction to Neural Networks/288 - The Linear model with Multiple Inputs and Multiple Outputs.mp4 21.7 MB
11 - Probability Bayesian Inference/47 - The Law of Total Probability.mp4 21.7 MB
44 - Deep Learning TensorFlow 20 Introduction/301 - TensorFlow Outline and Comparison with Other Libraries.mp4 21.7 MB
40 - Part 6 Mathematics/273 - Linear Algebra and Geometry.mp4 21.3 MB
11 - Probability Bayesian Inference/45 - Dependence and Independence of Sets.mp4 21.3 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/381 - MNIST Relevant Packages.mp4 21.3 MB
52 - Deep Learning Conclusion/368 - An Overview of nonNN Approaches.mp4 21.3 MB
29 - Python Iterations/170 - For Loops.mp4 21.2 MB
62 - Appendix Additional Python Tools/471 - Introduction to Nested For Loops.mp4 21.1 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - The Linear Regression Model.mp4 20.9 MB
12 - Probability Distributions/56 - Discrete Distributions The Bernoulli Distribution.mp4 20.8 MB
37 - Advanced Statistical Methods Cluster Analysis/249 - Introduction to Cluster Analysis.mp4 20.7 MB
10 - Probability Combinatorics/35 - Symmetry of Combinations.mp4 20.6 MB
18 - Statistics Inferential Statistics Confidence Intervals/107 - Students T Distribution.mp4 20.6 MB
64 - Appendix Working with Text Files in Python/501 - Importing Data with indexcol.mp4 20.6 MB
44 - Deep Learning TensorFlow 20 Introduction/302 - TensorFlow 1 vs TensorFlow 2.mp4 20.4 MB
10 - Probability Combinatorics/32 - Solving Variations with Repetition.mp4 20.4 MB
50 - Deep Learning Classifying on the MNIST Dataset/347 - MNIST Select the Loss and the Optimizer.mp4 20.2 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Exploring the Problem with a Machine Learning Mindset.mp4 19.9 MB
10 - Probability Combinatorics/36 - Solving Combinations with Separate Sample Spaces.mp4 19.8 MB
58 - Case Study Preprocessing the Absenteeismdata/429 - Reordering Columns in a Pandas DataFrame in Python.mp4 19.6 MB
42 - Deep Learning Introduction to Neural Networks/285 - Types of Machine Learning.mp4 19.0 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/211 - Multiple Linear Regression with sklearn.mp4 19.0 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/385 - MNIST Batching and Early Stopping.mp4 18.9 MB
22 - Part 4 Introduction to Python/138 - Why Python.mp4 18.9 MB
15 - Statistics Descriptive Statistics/77 - The Histogram.mp4 18.7 MB
41 - Part 7 Deep Learning/282 - What to Expect from this Part.mp4 18.4 MB
51 - Deep Learning Business Case Example/356 - Business Case Load the Preprocessed Data.mp4 18.4 MB
18 - Statistics Inferential Statistics Confidence Intervals/113 - Confidence intervals Two means Independent Samples Part 1.mp4 18.3 MB
57 - Case Study Whats Next in the Course/408 - The Business Task.mp4 18.2 MB
35 - Advanced Statistical Methods Practical Example Linear Regression/227 - Practical Example Linear Regression Part 3.mp4 18.2 MB
38 - Advanced Statistical Methods KMeans Clustering/256 - Clustering Categorical Data.mp4 18.1 MB
64 - Appendix Working with Text Files in Python/493 - Plain Text Files Flat Files and More.mp4 18.0 MB
58 - Case Study Preprocessing the Absenteeismdata/415 - Using a Statistical Approach towards the Solution to the Exercise.mp4 17.9 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/373 - Actual Introduction to TensorFlow.mp4 17.5 MB
36 - Advanced Statistical Methods Logistic Regression/241 - What do the Odds Actually Mean.mp4 17.5 MB
5 - The Field of Data Science Popular Data Science Techniques/14 - Real Life Examples of Big Data.mp4 17.3 MB
27 - Python Python Functions/165 - Builtin Functions in Python.mp4 17.3 MB
49 - Deep Learning Preprocessing/336 - Standardization.mp4 17.1 MB
30 - Python Advanced Python Tools/179 - Importing Modules in Python.mp4 16.9 MB
11 - Probability Bayesian Inference/48 - The Additive Rule.mp4 16.9 MB
63 - Appendix pandas Fundamentals/482 - Introduction to pandas DataFrames Part I.mp4 16.9 MB
10 - Probability Combinatorics/33 - Solving Variations without Repetition.mp4 16.9 MB
64 - Appendix Working with Text Files in Python/490 - Structured SemiStructured and Unstructured Data.mp4 16.6 MB
40 - Part 6 Mathematics/271 - What is a Matrix.mp4 16.5 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/310 - What is a Deep Net.mp4 16.5 MB
11 - Probability Bayesian Inference/42 - Intersection of Sets.mp4 16.5 MB
64 - Appendix Working with Text Files in Python/488 - An Introduction to Working with Files in Python.mp4 16.5 MB
38 - Advanced Statistical Methods KMeans Clustering/261 - To Standardize or not to Standardize.mp4 16.0 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/191 - RSquared.mp4 15.9 MB
12 - Probability Distributions/63 - Continuous Distributions The ChiSquared Distribution.mp4 15.9 MB
2 - The Field of Data Science The Various Data Science Disciplines/5 - What is the difference between Analysis and Analytics.mp4 15.8 MB
51 - Deep Learning Business Case Example/361 - Business Case Testing the Model.mp4 15.8 MB
23 - Python Variables and Data Types/143 - Variables.mp4 15.7 MB
38 - Advanced Statistical Methods KMeans Clustering/253 - KMeans Clustering.mp4 15.6 MB
65 - Bonus Lecture/517 - 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 15.6 MB
64 - Appendix Working with Text Files in Python/491 - Text Files and Data Connectivity.mp4 15.5 MB
63 - Appendix pandas Fundamentals/478 - Working with Methods in Python Part II.mp4 15.5 MB
11 - Probability Bayesian Inference/44 - Mutually Exclusive Sets.mp4 15.2 MB
46 - Deep Learning Overfitting/318 - What is Overfitting.mp4 15.1 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/374 - Types of File Formats supporting Tensors.mp4 15.1 MB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Basic NN Example Part 1.mp4 15.0 MB
12 - Probability Distributions/55 - Discrete Distributions The Uniform Distribution.mp4 15.0 MB
46 - Deep Learning Overfitting/323 - Early Stopping or When to Stop Training.mp4 14.9 MB
24 - Python Basic Python Syntax/146 - Using Arithmetic Operators in Python.mp4 14.9 MB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/445 - Selecting the Inputs for the Logistic Regression.mp4 14.8 MB
44 - Deep Learning TensorFlow 20 Introduction/304 - Types of File Formats Supporting TensorFlow.mp4 14.8 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/205 - What is sklearn and How is it Different from Other Packages.mp4 14.7 MB
38 - Advanced Statistical Methods KMeans Clustering/260 - Pros and Cons of KMeans Clustering.mp4 14.7 MB
36 - Advanced Statistical Methods Logistic Regression/236 - Building a Logistic Regression.mp4 14.7 MB
10 - Probability Combinatorics/31 - Simple Operations with Factorials.mp4 14.6 MB
42 - Deep Learning Introduction to Neural Networks/283 - Introduction to Neural Networks.mp4 14.5 MB
46 - Deep Learning Overfitting/321 - Training Validation and Test Datasets.mp4 14.4 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/327 - Stochastic Gradient Descent.mp4 14.4 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/315 - Backpropagation.mp4 14.3 MB
27 - Python Python Functions/160 - How to Create a Function with a Parameter.mp4 14.2 MB
52 - Deep Learning Conclusion/363 - Summary on What Youve Learned.mp4 14.2 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/187 - Using Seaborn for Graphs.mp4 14.2 MB
12 - Probability Distributions/62 - Continuous Distributions The Students T Distribution.mp4 14.1 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/198 - A2 No Endogeneity.mp4 13.9 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/189 - Decomposition of Variability.mp4 13.9 MB
12 - Probability Distributions/54 - Characteristics of Discrete Distributions.mp4 13.9 MB
17 - Statistics Inferential Statistics Fundamentals/98 - The Standard Normal Distribution.mp4 13.8 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/314 - Activation Functions Softmax Activation.mp4 13.7 MB
39 - Advanced Statistical Methods Other Types of Clustering/268 - Types of Clustering.mp4 13.6 MB
64 - Appendix Working with Text Files in Python/495 - Common Naming Conventions.mp4 13.6 MB
42 - Deep Learning Introduction to Neural Networks/292 - Common Objective Functions CrossEntropy Loss.mp4 13.5 MB
46 - Deep Learning Overfitting/320 - What is Validation.mp4 13.3 MB
47 - Deep Learning Initialization/324 - What is Initialization.mp4 13.1 MB
37 - Advanced Statistical Methods Cluster Analysis/251 - Difference between Classification and Clustering.mp4 13.1 MB
40 - Part 6 Mathematics/272 - Scalars and Vectors.mp4 12.9 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/313 - Activation Functions.mp4 12.9 MB
64 - Appendix Working with Text Files in Python/489 - File vs File Object Reading vs Parsing Data.mp4 12.8 MB
50 - Deep Learning Classifying on the MNIST Dataset/340 - MNIST How to Tackle the MNIST.mp4 12.8 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/332 - Adaptive Learning Rate Schedules AdaGrad and RMSprop.mp4 12.7 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/380 - MNIST How to Tackle the MNIST.mp4 12.4 MB
22 - Part 4 Introduction to Python/139 - Why Jupyter.mp4 12.2 MB
49 - Deep Learning Preprocessing/334 - Preprocessing Introduction.mp4 12.2 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/316 - Backpropagation Picture.mp4 12.2 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/200 - A4 No Autocorrelation.mp4 11.9 MB
30 - Python Advanced Python Tools/176 - Object Oriented Programming.mp4 11.9 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/195 - Test for Significance of the Model FTest.mp4 11.8 MB
27 - Python Python Functions/161 - Defining a Function in Python Part II.mp4 11.5 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/201 - A5 No Multicollinearity.mp4 11.2 MB
49 - Deep Learning Preprocessing/338 - Binary and OneHot Encoding.mp4 11.2 MB
18 - Statistics Inferential Statistics Confidence Intervals/117 - Confidence intervals Two means Independent Samples Part 3.mp4 11.0 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/333 - Adam Adaptive Moment Estimation.mp4 11.0 MB
42 - Deep Learning Introduction to Neural Networks/289 - Graphical Representation of Simple Neural Networks.mp4 10.8 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/216 - Creating a Summary Table with Pvalues.mp4 10.7 MB
52 - Deep Learning Conclusion/367 - An Overview of RNNs.mp4 10.6 MB
36 - Advanced Statistical Methods Logistic Regression/246 - Underfitting and Overfitting.mp4 10.5 MB
42 - Deep Learning Introduction to Neural Networks/286 - The Linear Model Linear Algebraic Version.mp4 10.5 MB
42 - Deep Learning Introduction to Neural Networks/287 - The Linear Model with Multiple Inputs.mp4 10.5 MB
42 - Deep Learning Introduction to Neural Networks/284 - Training the Model.mp4 10.5 MB
36 - Advanced Statistical Methods Logistic Regression/233 - Introduction to Logistic Regression.mp4 10.4 MB
23 - Python Variables and Data Types/144 - Numbers and Boolean Values in Python.mp4 10.2 MB
58 - Case Study Preprocessing the Absenteeismdata/424 - More on Dummy Variables A Statistical Perspective.mp4 10.2 MB
17 - Statistics Inferential Statistics Fundamentals/101 - Standard error.mp4 10.1 MB
27 - Python Python Functions/163 - Conditional Statements and Functions.mp4 9.9 MB
40 - Part 6 Mathematics/277 - Errors when Adding Matrices.mp4 9.5 MB
10 - Probability Combinatorics/29 - Fundamentals of Combinatorics.mp4 9.4 MB
22 - Part 4 Introduction to Python/141 - Understanding Jupyters Interface the Notebook Dashboard.mp4 9.3 MB
46 - Deep Learning Overfitting/322 - NFold Cross Validation.mp4 9.3 MB
53 - Appendix Deep Learning TensorFlow 1 Introduction/370 - How to Install TensorFlow 1.mp4 8.8 MB
47 - Deep Learning Initialization/325 - Types of Simple Initializations.mp4 8.8 MB
26 - Python Conditional Statements/156 - The ELSE Statement.mp4 8.7 MB
26 - Python Conditional Statements/155 - The IF Statement.mp4 8.7 MB
44 - Deep Learning TensorFlow 20 Introduction/303 - A Note on TensorFlow 2 Syntax.mp4 8.7 MB
12 - Probability Distributions/66 - FIFA19-post.csv 8.6 MB
12 - Probability Distributions/66 - FIFA19.csv 8.6 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/192 - Multiple Linear Regression.mp4 8.3 MB
42 - Deep Learning Introduction to Neural Networks/290 - What is the Objective Function.mp4 8.2 MB
47 - Deep Learning Initialization/326 - StateoftheArt Method Xavier Glorot Initialization.mp4 8.2 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/222 - Underfitting and Overfitting.mp4 8.2 MB
42 - Deep Learning Introduction to Neural Networks/291 - Common Objective Functions L2norm Loss.mp4 7.9 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/309 - What is a Layer.mp4 7.8 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/196 - OLS Assumptions.mp4 7.8 MB
58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 7.6 MB
34 - Advanced Statistical Methods Linear Regression with sklearn/206 - How are we Going to Approach this Section.mp4 7.5 MB
64 - Appendix Working with Text Files in Python/494 - Text Files of Fixed Width.mp4 7.3 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/399 - Business Case Testing the Model.mp4 7.2 MB
37 - Advanced Statistical Methods Cluster Analysis/252 - Math Prerequisites.mp4 7.2 MB
49 - Deep Learning Preprocessing/337 - Preprocessing Categorical Data.mp4 7.2 MB
30 - Python Advanced Python Tools/178 - What is the Standard Library.mp4 7.2 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/329 - Momentum.mp4 7.1 MB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/379 - MNIST What is the MNIST Dataset.mp4 7.0 MB
2 - The Field of Data Science The Various Data Science Disciplines/7 - 365-DataScience.png 6.9 MB
2 - The Field of Data Science The Various Data Science Disciplines/8 - 365-DataScience.png 6.9 MB
26 - Python Conditional Statements/158 - A Note on Boolean Values.mp4 6.8 MB
52 - Deep Learning Conclusion/364 - Whats Further out there in terms of Machine Learning.mp4 6.8 MB
50 - Deep Learning Classifying on the MNIST Dataset/339 - MNIST The Dataset.mp4 6.7 MB
25 - Python Other Python Operators/153 - Comparison Operators.mp4 6.6 MB
29 - Python Iterations/174 - Conditional Statements Functions and Loops.mp4 6.5 MB
55 - Appendix Deep Learning TensorFlow 1 Business Case/391 - Business Case Outlining the Solution.mp4 6.3 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/182 - Correlation vs Regression.mp4 5.6 MB
31 - Part 5 Advanced Statistical Methods in Python/180 - Introduction to Regression Analysis.mp4 5.5 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/328 - Problems with Gradient Descent.mp4 5.4 MB
38 - Advanced Statistical Methods KMeans Clustering/262 - Relationship between Clustering and Regression.mp4 5.4 MB
17 - Statistics Inferential Statistics Fundamentals/95 - Introduction.mp4 5.1 MB
27 - Python Python Functions/159 - Defining a Function in Python.mp4 5.1 MB
27 - Python Python Functions/162 - How to Use a Function within a Function.mp4 5.1 MB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/197 - A1 Linearity.mp4 5.1 MB
27 - Python Python Functions/164 - Functions Containing a Few Arguments.mp4 4.9 MB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/331 - Learning Rate Schedules Visualized.mp4 4.8 MB
49 - Deep Learning Preprocessing/335 - Types of Basic Preprocessing.mp4 4.7 MB
51 - Deep Learning Business Case Example/352 - Business Case Outlining the Solution.mp4 4.5 MB
24 - Python Basic Python Syntax/152 - Structuring with Indentation.mp4 4.4 MB
24 - Python Basic Python Syntax/147 - The Double Equality Sign.mp4 4.2 MB
24 - Python Basic Python Syntax/149 - Add Comments.mp4 3.8 MB
24 - Python Basic Python Syntax/151 - Indexing Elements.mp4 3.6 MB
32 - Advanced Statistical Methods Linear Regression with StatsModels/183 - Geometrical Representation of the Linear Regression Model.mp4 3.2 MB
30 - Python Advanced Python Tools/177 - Modules and Packages.mp4 3.0 MB
64 - Appendix Working with Text Files in Python/516 - Working with Text Files in Python Conclusion.mp4 3.0 MB
24 - Python Basic Python Syntax/148 - How to Reassign Values.mp4 2.8 MB
22 - Part 4 Introduction to Python/137 - Introduction-to-Python-Course-Notes.pdf 2.2 MB
23 - Python Variables and Data Types/143 - Introduction-to-Python-Course-Notes.pdf 2.2 MB
19 - Statistics Practical Example Inferential Statistics/119 - 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.8 MB
24 - Python Basic Python Syntax/150 - Understanding Line Continuation.mp4 1.7 MB
19 - Statistics Practical Example Inferential Statistics/118 - 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.7 MB
19 - Statistics Practical Example Inferential Statistics/119 - 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.7 MB
20 - Statistics Hypothesis Testing/126 - Online-p-value-calculator.pdf 1.2 MB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/309 - Course-Notes-Section-6.pdf 936.4 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/310 - Course-Notes-Section-6.pdf 936.4 KB
11 - Probability Bayesian Inference/51 - CDS-2017-2018-Hamilton.pdf 845.3 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/231 - sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 711.0 KB
51 - Deep Learning Business Case Example/351 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - Audiobooks-data.csv 710.8 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/231 - sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 698.4 KB
20 - Statistics Hypothesis Testing/120 - Course-notes-hypothesis-testing.pdf 656.4 KB
20 - Statistics Hypothesis Testing/122 - Course-notes-hypothesis-testing.pdf 656.4 KB
64 - Appendix Working with Text Files in Python/488 - Common-Naming-Conventions.pdf 643.8 KB
64 - Appendix Working with Text Files in Python/495 - Common-Naming-Conventions.pdf 643.8 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Shortcuts-for-Jupyter.pdf 619.2 KB
44 - Deep Learning TensorFlow 20 Introduction/300 - Shortcuts-for-Jupyter.pdf 619.2 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/373 - Shortcuts-for-Jupyter.pdf 619.2 KB
42 - Deep Learning Introduction to Neural Networks/283 - Course-Notes-Section-2.pdf 578.1 KB
42 - Deep Learning Introduction to Neural Networks/284 - Course-Notes-Section-2.pdf 578.1 KB
14 - Part 3 Statistics/70 - Course-notes-descriptive-statistics.pdf 482.2 KB
15 - Statistics Descriptive Statistics/71 - Course-notes-descriptive-statistics.pdf 482.2 KB
12 - Probability Distributions/52 - Course-Notes-Probability-Distributions.pdf 463.9 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/229 - sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 407.6 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/229 - sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 397.2 KB
11 - Probability Bayesian Inference/40 - Course-Notes-Bayesian-Inference.pdf 386.0 KB
17 - Statistics Inferential Statistics Fundamentals/95 - Course-notes-inferential-statistics.pdf 382.3 KB
17 - Statistics Inferential Statistics Fundamentals/96 - Course-notes-inferential-statistics.pdf 382.3 KB
9 - Part 2 Probability/25 - Course-Notes-Basic-Probability.pdf 371.1 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/228 - sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 370.2 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 351.5 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/228 - sklearn-Dummies-and-VIF-Exercise.ipynb 344.6 KB
12 - Probability Distributions/59 - Solving-Integrals.pdf 343.9 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 343.6 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/225 - sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 335.6 KB
36 - Advanced Statistical Methods Logistic Regression/233 - Course-Notes-Logistic-Regression.pdf 335.2 KB
36 - Advanced Statistical Methods Logistic Regression/234 - Course-Notes-Logistic-Regression.pdf 335.2 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/225 - sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 328.7 KB
2 - The Field of Data Science The Various Data Science Disciplines/6 - 365-DataScience-Diagram.pdf 323.1 KB
2 - The Field of Data Science The Various Data Science Disciplines/7 - 365-DataScience-Diagram.pdf 323.1 KB
13 - Probability Probability in Other Fields/69 - Probability-Cheat-Sheet.pdf 320.3 KB
31 - Part 5 Advanced Statistical Methods in Python/180 - Course-notes-regression-analysis.pdf 312.2 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - Course-notes-regression-analysis.pdf 312.2 KB
1 - Part 1 Introduction/3 - FAQ-The-Data-Science-Course.pdf 306.1 KB
15 - Statistics Descriptive Statistics/74 - Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
15 - Statistics Descriptive Statistics/78 - Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
10 - Probability Combinatorics/39 - Additional-Exercises-Combinatorics-Solutions.pdf 245.7 KB
10 - Probability Combinatorics/29 - Course-Notes-Combinatorics.pdf 226.1 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/224 - 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/225 - 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/228 - 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/229 - 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/231 - 1.04.Real-life-example.csv 219.8 KB
64 - Appendix Working with Text Files in Python/505 - Lending-company.json 213.5 KB
37 - Advanced Statistical Methods Cluster Analysis/249 - Course-Notes-Cluster-Analysis.pdf 208.7 KB
37 - Advanced Statistical Methods Cluster Analysis/250 - Course-Notes-Cluster-Analysis.pdf 208.7 KB
10 - Probability Combinatorics/34 - Combinations-With-Repetition.pdf 207.4 KB
13 - Probability Probability in Other Fields/67 - Probability-in-Finance-Solutions.pdf 184.5 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/317 - Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/224 - sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 171.4 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/224 - sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 166.9 KB
63 - Appendix pandas Fundamentals/475 - Sales-products.csv 152.3 KB
63 - Appendix pandas Fundamentals/487 - Sales-products.csv 152.3 KB
16 - Statistics Practical Example Descriptive Statistics/93 - 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 146.5 KB
16 - Statistics Practical Example Descriptive Statistics/94 - 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146.4 KB
12 - Probability Distributions/58 - Poisson-Expected-Value-and-Variance.pdf 146.0 KB
12 - Probability Distributions/60 - Normal-Distribution-Exp-and-Var.pdf 144.1 KB
58 - Case Study Preprocessing the Absenteeismdata/410 - data-preprocessing-homework.pdf 134.5 KB
16 - Statistics Practical Example Descriptive Statistics/94 - 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.3 KB
63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Solutions.ipynb 118.3 KB
63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Solutions.ipynb 118.3 KB
64 - Appendix Working with Text Files in Python/498 - Lending-company-single-column-data.csv 114.5 KB
63 - Appendix pandas Fundamentals/475 - Lending-company.csv 112.4 KB
63 - Appendix pandas Fundamentals/487 - Lending-company.csv 112.4 KB
64 - Appendix Working with Text Files in Python/498 - Lending-company.csv 112.4 KB
36 - Advanced Statistical Methods Logistic Regression/248 - Testing-the-Model-Solution.ipynb 111.1 KB
13 - Probability Probability in Other Fields/67 - Probability-in-Finance-Homework.pdf 110.7 KB
10 - Probability Combinatorics/39 - Additional-Exercises-Combinatorics.pdf 106.6 KB
64 - Appendix Working with Text Files in Python/507 - Lending-company.xlsx 93.1 KB
10 - Probability Combinatorics/35 - Symmetry-Explained.pdf 85.0 KB
44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 84.4 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.d.Solution.ipynb 84.1 KB
44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 83.7 KB
44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-example-All-exercises.ipynb 83.6 KB
44 - Deep Learning TensorFlow 20 Introduction/307 - TensorFlow-Minimal-example-complete-with-comments.ipynb 82.3 KB
36 - Advanced Statistical Methods Logistic Regression/245 - Calculating-the-Accuracy-of-the-Model-Solution.ipynb 81.2 KB
44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 77.5 KB
44 - Deep Learning TensorFlow 20 Introduction/307 - TensorFlow-Minimal-example-complete.ipynb 76.9 KB
44 - Deep Learning TensorFlow 20 Introduction/306 - TensorFlow-Minimal-example-Part3.ipynb 76.5 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.c.Solution.ipynb 70.1 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-1-Solution.ipynb 69.0 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-5-Solution.ipynb 68.9 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.a.Solution.ipynb 67.9 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.b.Solution.ipynb 67.7 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-4-Solution.ipynb 66.5 KB
60 - Case Study Loading the absenteeismmodule/459 - Absenteeism-Exercise-Integration.ipynb 62.4 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-6-Solution.ipynb 61.8 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-6.ipynb 61.8 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-2-Solution.ipynb 61.4 KB
64 - Appendix Working with Text Files in Python/512 - Lending-Company-Saving.csv 58.4 KB
21 - Statistics Practical Example Hypothesis Testing/135 - 4.10.Hypothesis-testing-section-practical-example.xlsx 51.9 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 50.0 KB
21 - Statistics Practical Example Hypothesis Testing/136 - 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44.3 KB
21 - Statistics Practical Example Hypothesis Testing/136 - 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 43.7 KB
42 - Deep Learning Introduction to Neural Networks/293 - GD-function-example.xlsx 42.3 KB
15 - Statistics Descriptive Statistics/74 - 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 41.1 KB
15 - Statistics Descriptive Statistics/80 - 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 40.4 KB
15 - Statistics Descriptive Statistics/83 - 2.8.Skewness-lesson.xlsx 34.6 KB
58 - Case Study Preprocessing the Absenteeismdata/410 - Absenteeism-data.csv 32.0 KB
63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Exercises.ipynb 31.0 KB
63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Exercises.ipynb 31.0 KB
15 - Statistics Descriptive Statistics/73 - 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 30.8 KB
11 - Probability Bayesian Inference/51 - Bayesian-Homework-Solutions.pdf 30.4 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/220 - sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 29.8 KB
64 - Appendix Working with Text Files in Python/502 - Lending-Company-Numeric-Data.csv 29.5 KB
15 - Statistics Descriptive Statistics/90 - 2.11.Covariance-exercise-solution.xlsx 29.5 KB
15 - Statistics Descriptive Statistics/92 - 2.12.Correlation-exercise-solution.xlsx 29.5 KB
15 - Statistics Descriptive Statistics/92 - 2.12.Correlation-exercise.xlsx 29.3 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Absenteeism-preprocessed.csv 29.1 KB
58 - Case Study Preprocessing the Absenteeismdata/410 - df-preprocessed.csv 29.1 KB
64 - Appendix Working with Text Files in Python/502 - Lending-Company-Numeric-Data-NAN.csv 28.6 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/208 - sklearn-Simple-Linear-Regression-with-comments.ipynb 28.4 KB
44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.0 KB
64 - Appendix Working with Text Files in Python/488 - Working-with-Text-Files-Lectures.ipynb 27.6 KB
64 - Appendix Working with Text Files in Python/516 - Working-with-Text-Files-Lectures.ipynb 27.6 KB
11 - Probability Bayesian Inference/51 - Bayesian-Homework.pdf 27.3 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.0 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 26.7 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/210 - Simple-Linear-Regression-with-sklearn-Exercise-Solution.ipynb 26.6 KB
12 - Probability Distributions/66 - A Practical Example of Probability Distributions English.srt 26.5 KB
16 - Statistics Practical Example Descriptive Statistics/93 - Practical Example Descriptive Statistics English.srt 26.4 KB
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53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 25.5 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 25.5 KB
62 - Appendix Additional Python Tools/469 - Additional-Python-Tools-Solutions.ipynb 25.5 KB
62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Solutions.ipynb 25.5 KB
11 - Probability Bayesian Inference/51 - A Practical Example of Bayesian Inference English.srt 25.2 KB
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64 - Appendix Working with Text Files in Python/504 - Importing-Text-Data-DSc-Solution.ipynb 24.4 KB
17 - Statistics Inferential Statistics Fundamentals/99 - 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.0 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 23.6 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/220 - sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.0 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 21.7 KB
1 - Part 1 Introduction/3 - Download All Resources and Important FAQ.html 21.3 KB
63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Lectures.ipynb 21.3 KB
63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Lectures.ipynb 21.3 KB
50 - Deep Learning Classifying on the MNIST Dataset/349 - 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 20.6 KB
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15 - Statistics Descriptive Statistics/90 - 2.11.Covariance-exercise.xlsx 20.2 KB
12 - Probability Distributions/66 - Daily-Views-post.xlsx 20.2 KB
64 - Appendix Working with Text Files in Python/509 - Importing-Data-with-the-pandas-Squeeze-Method.ipynb 20.1 KB
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15 - Statistics Descriptive Statistics/84 - 2.8.Skewness-exercise-solution.xlsx 19.8 KB
51 - Deep Learning Business Case Example/358 - TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 19.7 KB
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17 - Statistics Inferential Statistics Fundamentals/96 - 3.2.What-is-a-distribution-lesson.xlsx 19.5 KB
10 - Probability Combinatorics/39 - A Practical Example of Combinatorics English.srt 19.3 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/224 - Practical Example Linear Regression Part 1 English.srt 18.7 KB
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64 - Appendix Working with Text Files in Python/502 - Importing Data with loadtxt and genfromtxt English.srt 18.4 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/203 - Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb 18.0 KB
39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps-with-comments.ipynb 17.7 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - TensorFlow-MNIST-around-98-percent-accuracy.ipynb 17.7 KB
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51 - Deep Learning Business Case Example/354 - Business Case Preprocessing the Data English.srt 17.1 KB
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64 - Appendix Working with Text Files in Python/496 - Importing Text Files open English.srt 16.9 KB
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34 - Advanced Statistical Methods Linear Regression with sklearn/219 - SKLEAR-1.IPY 16.8 KB
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34 - Advanced Statistical Methods Linear Regression with sklearn/216 - sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb 16.6 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/221 - sklearn-Feature-Scaling-Exercise-Solution.ipynb 16.3 KB
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62 - Appendix Additional Python Tools/473 - List Comprehensions English.srt 16.0 KB
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62 - Appendix Additional Python Tools/469 - Using the format Method English.srt 15.8 KB
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12 - Probability Distributions/66 - Customers-Membership-post.xlsx 15.6 KB
2 - The Field of Data Science The Various Data Science Disciplines/7 - Continuing with BI ML and AI English.srt 15.6 KB
15 - Statistics Descriptive Statistics/78 - 2.5.The-Histogram-exercise.xlsx 15.5 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/388 - TensorFlow-MNIST-Exercises-All.ipynb 15.5 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/217 - sklearn-Multiple-Linear-Regression-Exercise-Solution.ipynb 15.4 KB
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38 - Advanced Statistical Methods KMeans Clustering/267 - Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.3 KB
15 - Statistics Descriptive Statistics/74 - 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.2 KB
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35 - Advanced Statistical Methods Practical Example Linear Regression/229 - Practical Example Linear Regression Part 4 English.srt 14.9 KB
50 - Deep Learning Classifying on the MNIST Dataset/349 - 1.TensorFlow-MNIST-Width-Solution.ipynb 14.8 KB
50 - Deep Learning Classifying on the MNIST Dataset/349 - 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.7 KB
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50 - Deep Learning Classifying on the MNIST Dataset/350 - TensorFlow-MNIST-complete-with-comments.ipynb 14.5 KB
5 - The Field of Data Science Popular Data Science Techniques/17 - Techniques for Working with Traditional Methods English.srt 14.4 KB
20 - Statistics Hypothesis Testing/130 - 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.4 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.4 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.4 KB
40 - Part 6 Mathematics/281 - Why is Linear Algebra Useful English.srt 14.4 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.3 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.3 KB
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54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.0 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-All-Exercises.ipynb 14.0 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/231 - Practical Example Linear Regression Part 5 English.srt 13.9 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 13.9 KB
51 - Deep Learning Business Case Example/351 - Business Case Exploring the Dataset and Identifying Predictors English.srt 13.9 KB
2 - The Field of Data Science The Various Data Science Disciplines/6 - Business Analytics Data Analytics and Data Science An Introduction English.srt 13.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Business Case Getting Acquainted with the Dataset English.srt 13.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/112 - 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 13.7 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Basic NN Example Part 4 English.srt 13.7 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/216 - sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 13.7 KB
5 - The Field of Data Science Popular Data Science Techniques/11 - Techniques for Working with Traditional Data English.srt 13.7 KB
56 - Software Integration/404 - Taking a Closer Look at APIs English.srt 13.6 KB
63 - Appendix pandas Fundamentals/475 - Introduction to pandas Series English.srt 13.6 KB
63 - Appendix pandas Fundamentals/475 - Location.csv 13.5 KB
63 - Appendix pandas Fundamentals/487 - Location.csv 13.5 KB
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62 - Appendix Additional Python Tools/469 - Additional-Python-Tools-Lectures.ipynb 13.5 KB
62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Lectures.ipynb 13.5 KB
64 - Appendix Working with Text Files in Python/515 - Saving-Data-NP-Solution.ipynb 13.4 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - Multiple-Linear-Regression-Exercise-Solution.ipynb 13.4 KB
58 - Case Study Preprocessing the Absenteeismdata/425 - Classifying the Various Reasons for Absence English.srt 13.2 KB
63 - Appendix pandas Fundamentals/485 - Data Selection in pandas DataFrames English.srt 13.2 KB
15 - Statistics Descriptive Statistics/76 - 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.2 KB
58 - Case Study Preprocessing the Absenteeismdata/420 - Obtaining Dummies from a Single Feature English.srt 13.1 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - MNIST Learning English.srt 13.1 KB
62 - Appendix Additional Python Tools/474 - Anonymous Lambda Functions English.srt 13.1 KB
12 - Probability Distributions/53 - Types of Probability Distributions English.srt 13.1 KB
28 - Python Sequences/166 - Lists English.srt 13.0 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - 12.9.TensorFlow-MNIST-with-comments.ipynb 13.0 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/214 - sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.0 KB
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20 - Statistics Hypothesis Testing/130 - 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 12.8 KB
61 - Case Study Analyzing the Predicted Outputs in Tableau/464 - Analyzing Age vs Probability in Tableau English.srt 12.8 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 12.7 KB
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34 - Advanced Statistical Methods Linear Regression with sklearn/215 - sklearn-How-to-properly-include-p-values.ipynb 12.7 KB
13 - Probability Probability in Other Fields/67 - Probability in Finance English.srt 12.6 KB
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33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dealing with Categorical Data Dummy Variables English.srt 12.6 KB
50 - Deep Learning Classifying on the MNIST Dataset/348 - TensorFlow-MNIST-Part6-with-comments.ipynb 12.5 KB
38 - Advanced Statistical Methods KMeans Clustering/254 - A Simple Example of Clustering English.srt 12.3 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/223 - Train Test Split Explained English.srt 12.2 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - 5.6.TensorFlow-Minimal-example-complete.ipynb 12.1 KB
64 - Appendix Working with Text Files in Python/503 - Importing Data Partial Cleaning While Importing Data English.srt 12.1 KB
18 - Statistics Inferential Statistics Confidence Intervals/104 - Confidence Intervals Population Variance Known Zscore English.srt 12.0 KB
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50 - Deep Learning Classifying on the MNIST Dataset/344 - MNIST Preprocess the Data Shuffle and Batch English.srt 11.9 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/218 - sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 11.7 KB
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36 - Advanced Statistical Methods Logistic Regression/244 - Accuracy-with-comments.ipynb 11.7 KB
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64 - Appendix Working with Text Files in Python/502 - Importing-Text-Data-with-NumPy-Complete.ipynb 11.6 KB
22 - Part 4 Introduction to Python/140 - Installing Python and Jupyter English.srt 11.6 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/218 - Feature Scaling Standardization English.srt 11.5 KB
64 - Appendix Working with Text Files in Python/500 - Importing csv Files Part III English.srt 11.5 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - MNIST Model Outline English.srt 11.5 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.5 KB
3 - The Field of Data Science Connecting the Data Science Disciplines/9 - Applying Traditional Data Big Data BI Traditional Data Science and ML English.srt 11.5 KB
15 - Statistics Descriptive Statistics/75 - 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.4 KB
40 - Part 6 Mathematics/280 - Dot Product of Matrices English.srt 11.4 KB
12 - Probability Distributions/59 - Characteristics of Continuous Distributions English.srt 11.4 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Minimal-example-Part-4-Complete.ipynb 11.4 KB
56 - Software Integration/403 - What are Data Connectivity APIs and Endpoints English.srt 11.4 KB
38 - Advanced Statistical Methods KMeans Clustering/264 - Market Segmentation with Cluster Analysis Part 2 English.srt 11.4 KB
20 - Statistics Hypothesis Testing/134 - 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.4 KB
13 - Probability Probability in Other Fields/68 - Probability in Statistics English.srt 11.4 KB
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62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Exercises.ipynb 11.4 KB
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20 - Statistics Hypothesis Testing/132 - 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.2 KB
20 - Statistics Hypothesis Testing/125 - 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.2 KB
18 - Statistics Inferential Statistics Confidence Intervals/104 - 3.9.Population-variance-known-z-score-lesson.xlsx 11.2 KB
51 - Deep Learning Business Case Example/354 - TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
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28 - Python Sequences/169 - Dictionaries English.srt 11.1 KB
18 - Statistics Inferential Statistics Confidence Intervals/109 - 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.1 KB
9 - Part 2 Probability/25 - The Basic Probability Formula English.srt 11.1 KB
15 - Statistics Descriptive Statistics/86 - 2.9.Variance-exercise-solution.xlsx 11.1 KB
64 - Appendix Working with Text Files in Python/498 - Importing csv Files Part I English.srt 11.0 KB
58 - Case Study Preprocessing the Absenteeismdata/435 - Analyzing the Dates from the Initial Data Set English.srt 11.0 KB
20 - Statistics Hypothesis Testing/125 - 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.0 KB
50 - Deep Learning Classifying on the MNIST Dataset/347 - TensorFlow-MNIST-Part5-with-comments.ipynb 11.0 KB
42 - Deep Learning Introduction to Neural Networks/293 - Optimization Algorithm 1Parameter Gradient Descent English.srt 11.0 KB
15 - Statistics Descriptive Statistics/87 - 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.0 KB
20 - Statistics Hypothesis Testing/124 - 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.0 KB
12 - Probability Distributions/57 - Discrete Distributions The Binomial Distribution English.srt 11.0 KB
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15 - Statistics Descriptive Statistics/82 - 2.7.Mean-median-and-mode-exercise.xlsx 10.9 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/444 - Creating the Targets for the Logistic Regression English.srt 10.8 KB
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15 - Statistics Descriptive Statistics/86 - 2.9.Variance-exercise.xlsx 10.8 KB
21 - Statistics Practical Example Hypothesis Testing/135 - Practical Example Hypothesis Testing English.srt 10.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/108 - 3.11.Population-variance-unknown-t-score-lesson.xlsx 10.8 KB
29 - Python Iterations/172 - Lists with the range Function English.srt 10.8 KB
20 - Statistics Hypothesis Testing/132 - 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.8 KB
38 - Advanced Statistical Methods KMeans Clustering/267 - Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 10.7 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/447 - Splitting the Data for Training and Testing English.srt 10.7 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/450 - Interpreting the Coefficients for Our Problem English.srt 10.7 KB
20 - Statistics Hypothesis Testing/122 - Rejection Region and Significance Level English.srt 10.7 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.6 KB
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18 - Statistics Inferential Statistics Confidence Intervals/109 - 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.6 KB
62 - Appendix Additional Python Tools/472 - Triple Nested For Loops English.srt 10.6 KB
62 - Appendix Additional Python Tools/471 - Introduction to Nested For Loops English.srt 10.6 KB
44 - Deep Learning TensorFlow 20 Introduction/305 - Outlining the Model with TensorFlow 2 English.srt 10.6 KB
18 - Statistics Inferential Statistics Confidence Intervals/111 - Confidence intervals Two means Dependent samples English.srt 10.5 KB
20 - Statistics Hypothesis Testing/134 - 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.5 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - The Linear Regression Model English.srt 10.5 KB
15 - Statistics Descriptive Statistics/81 - 2.7.Mean-median-and-mode-lesson.xlsx 10.5 KB
50 - Deep Learning Classifying on the MNIST Dataset/346 - TensorFlow-MNIST-Part4-with-comments.ipynb 10.5 KB
12 - Probability Distributions/52 - Fundamentals of Probability Distributions English.srt 10.5 KB
18 - Statistics Inferential Statistics Confidence Intervals/111 - 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.5 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/214 - sklearn-Feature-Selection-with-F-regression.ipynb 10.4 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/212 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.4 KB
63 - Appendix pandas Fundamentals/486 - pandas DataFrames Indexing with iloc English.srt 10.4 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/225 - Practical Example Linear Regression Part 2 English.srt 10.4 KB
17 - Statistics Inferential Statistics Fundamentals/98 - 3.4.Standard-normal-distribution-lesson.xlsx 10.4 KB
58 - Case Study Preprocessing the Absenteeismdata/416 - Dropping a Column from a DataFrame in Python English.srt 10.4 KB
64 - Appendix Working with Text Files in Python/508 - Importing Data in Python an Important Exercise English.srt 10.4 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - First Regression in Python English.srt 10.3 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.3 KB
38 - Advanced Statistical Methods KMeans Clustering/257 - Categorical.csv 10.3 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/395 - Creating a Data Provider English.srt 10.3 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/213 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.3 KB
15 - Statistics Descriptive Statistics/85 - Variance English.srt 10.2 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - MNIST Results and Testing English.srt 10.2 KB
63 - Appendix pandas Fundamentals/475 - Region.csv 10.2 KB
63 - Appendix pandas Fundamentals/487 - Region.csv 10.2 KB
20 - Statistics Hypothesis Testing/124 - Test for the Mean Population Variance Known English.srt 10.2 KB
18 - Statistics Inferential Statistics Confidence Intervals/114 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.1 KB
51 - Deep Learning Business Case Example/359 - Business Case Setting an Early Stopping Mechanism English.srt 10.1 KB
15 - Statistics Descriptive Statistics/85 - 2.9.Variance-lesson.xlsx 10.1 KB
51 - Deep Learning Business Case Example/359 - TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.1 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - Basic NN Example with TF Inputs Outputs Targets Weights Biases English.srt 10.0 KB
51 - Deep Learning Business Case Example/355 - TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
6 - The Field of Data Science Popular Data Science Tools/22 - Necessary Programming Languages and Software Used in Data Science English.srt 10.0 KB
60 - Case Study Loading the absenteeismmodule/461 - Deploying the absenteeismmodule Part II English.srt 10.0 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
63 - Appendix pandas Fundamentals/483 - Introduction to pandas DataFrames Part II English.srt 10.0 KB
58 - Case Study Preprocessing the Absenteeismdata/436 - Extracting the Month Value from the Date Column English.srt 10.0 KB
50 - Deep Learning Classifying on the MNIST Dataset/348 - MNIST Learning English.srt 10.0 KB
29 - Python Iterations/173 - Conditional Statements and Loops English.srt 9.9 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - Basic NN Example with TF Model Output English.srt 9.8 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/213 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 9.8 KB
64 - Appendix Working with Text Files in Python/512 - Saving-Data-NP-Complete.ipynb 9.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/113 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 9.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/114 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 9.8 KB
22 - Part 4 Introduction to Python/142 - Prerequisites for Coding in the Jupyter Notebooks English.srt 9.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/116 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 9.8 KB
20 - Statistics Hypothesis Testing/129 - 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 9.8 KB
29 - Python Iterations/175 - How to Iterate over Dictionaries English.srt 9.8 KB
12 - Probability Distributions/66 - Customers-Membership.xlsx 9.7 KB
42 - Deep Learning Introduction to Neural Networks/294 - Optimization Algorithm nParameter Gradient Descent English.srt 9.7 KB
23 - Python Variables and Data Types/145 - Python Strings English.srt 9.6 KB
20 - Statistics Hypothesis Testing/131 - 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.6 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/219 - Feature Selection through Standardization of Weights English.srt 9.6 KB
61 - Case Study Analyzing the Predicted Outputs in Tableau/468 - Analyzing Transportation Expense vs Probability in Tableau English.srt 9.6 KB
11 - Probability Bayesian Inference/50 - Bayes Law English.srt 9.6 KB
12 - Probability Distributions/66 - Daily-Views.xlsx 9.5 KB
18 - Statistics Inferential Statistics Confidence Intervals/115 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.5 KB
15 - Statistics Descriptive Statistics/84 - 2.8.Skewness-exercise.xlsx 9.5 KB
39 - Advanced Statistical Methods Other Types of Clustering/269 - Dendrogram English.srt 9.5 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/207 - Simple Linear Regression with sklearn English.srt 9.5 KB
64 - Appendix Working with Text Files in Python/512 - Saving Your Data with NumPy Part I npy English.srt 9.5 KB
38 - Advanced Statistical Methods KMeans Clustering/258 - How to Choose the Number of Clusters English.srt 9.4 KB
64 - Appendix Working with Text Files in Python/505 - Importing Data from json Files English.srt 9.4 KB
38 - Advanced Statistical Methods KMeans Clustering/263 - Market Segmentation with Cluster Analysis Part 1 English.srt 9.4 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making-predictions-with-comments.ipynb 9.4 KB
64 - Appendix Working with Text Files in Python/492 - Importing Data in Python Principles English.srt 9.4 KB
28 - Python Sequences/168 - Tuples English.srt 9.4 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.4 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Adjusted RSquared English.srt 9.4 KB
2 - The Field of Data Science The Various Data Science Disciplines/4 - Data Science and Business Buzzwords Why are there so Many English.srt 9.3 KB
20 - Statistics Hypothesis Testing/133 - 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.3 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/452 - Interpreting the Coefficients of the Logistic Regression English.srt 9.3 KB
64 - Appendix Working with Text Files in Python/497 - Importing Text Files with open English.srt 9.3 KB
18 - Statistics Inferential Statistics Confidence Intervals/116 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.2 KB
63 - Appendix pandas Fundamentals/482 - Introduction to pandas DataFrames Part I English.srt 9.1 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/212 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.1 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/448 - Fitting the Model and Assessing its Accuracy English.srt 9.1 KB
44 - Deep Learning TensorFlow 20 Introduction/305 - TensorFlow-Minimal-example-Part2.ipynb 9.1 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/223 - sklearn-Train-Test-Split-with-comments.ipynb 9.0 KB
22 - Part 4 Introduction to Python/137 - Introduction to Programming English.srt 9.0 KB
12 - Probability Distributions/58 - Discrete Distributions The Poisson Distribution English.srt 9.0 KB
63 - Appendix pandas Fundamentals/477 - Working with Methods in Python Part I English.srt 9.0 KB
22 - Part 4 Introduction to Python/138 - Why Python English.srt 9.0 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - Business Case Model Outline English.srt 8.9 KB
50 - Deep Learning Classifying on the MNIST Dataset/346 - MNIST Outline the Model English.srt 8.9 KB
20 - Statistics Hypothesis Testing/120 - Null vs Alternative Hypothesis English.srt 8.9 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/199 - A3 Normality and Homoscedasticity English.srt 8.9 KB
13 - Probability Probability in Other Fields/69 - Probability in Data Science English.srt 8.9 KB
58 - Case Study Preprocessing the Absenteeismdata/412 - Checking the Content of the Data Set English.srt 8.9 KB
9 - Part 2 Probability/28 - Events and Their Complements English.srt 8.8 KB
30 - Python Advanced Python Tools/176 - Object Oriented Programming English.srt 8.7 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/214 - Feature Selection Fregression English.srt 8.7 KB
9 - Part 2 Probability/27 - Frequency English.srt 8.7 KB
9 - Part 2 Probability/26 - Computing Expected Values English.srt 8.7 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/211 - sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.7 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - Business Case Optimization English.srt 8.7 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.6 KB
56 - Software Integration/406 - Software Integration Explained English.srt 8.6 KB
46 - Deep Learning Overfitting/323 - Early Stopping or When to Stop Training English.srt 8.6 KB
50 - Deep Learning Classifying on the MNIST Dataset/345 - TensorFlow-MNIST-Part3-with-comments.ipynb 8.6 KB
51 - Deep Learning Business Case Example/355 - TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/311 - Digging into a Deep Net English.srt 8.6 KB
15 - Statistics Descriptive Statistics/79 - Cross Tables and Scatter Plots English.srt 8.6 KB
64 - Appendix Working with Text Files in Python/493 - Plain Text Files Flat Files and More English.srt 8.6 KB
26 - Python Conditional Statements/157 - The ELIF Statement English.srt 8.5 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/385 - 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.5 KB
58 - Case Study Preprocessing the Absenteeismdata/441 - Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.5 KB
44 - Deep Learning TensorFlow 20 Introduction/306 - Interpreting the Result and Extracting the Weights and Bias English.srt 8.5 KB
38 - Advanced Statistical Methods KMeans Clustering/259 - How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.5 KB
1 - Part 1 Introduction/1 - A Practical Example What You Will Learn in This Course English.srt 8.5 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/208 - Simple Linear Regression with sklearn A StatsModelslike Summary Table English.srt 8.5 KB
20 - Statistics Hypothesis Testing/129 - Test for the Mean Dependent Samples English.srt 8.5 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/191 - RSquared English.srt 8.4 KB
38 - Advanced Statistical Methods KMeans Clustering/265 - How is Clustering Useful English.srt 8.4 KB
29 - Python Iterations/170 - For Loops English.srt 8.4 KB
58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.3 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/296 - Basic NN Example Part 2 English.srt 8.3 KB
15 - Statistics Descriptive Statistics/73 - Categorical Variables Visualization Techniques English.srt 8.3 KB
36 - Advanced Statistical Methods Logistic Regression/248 - Bank-data-testing.csv 8.3 KB
64 - Appendix Working with Text Files in Python/513 - Saving Your Data with NumPy Part II npz English.srt 8.3 KB
38 - Advanced Statistical Methods KMeans Clustering/255 - Countries-exercise.csv 8.3 KB
38 - Advanced Statistical Methods KMeans Clustering/259 - Countries-exercise.csv 8.3 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/212 - Calculating the Adjusted RSquared in sklearn English.srt 8.3 KB
38 - Advanced Statistical Methods KMeans Clustering/253 - KMeans Clustering English.srt 8.2 KB
44 - Deep Learning TensorFlow 20 Introduction/300 - How to Install TensorFlow 20 English.srt 8.2 KB
36 - Advanced Statistical Methods Logistic Regression/247 - Testing the Model English.srt 8.1 KB
63 - Appendix pandas Fundamentals/484 - pandas DataFrames Common Attributes English.srt 8.1 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/454 - Testing the Model We Created English.srt 8.1 KB
50 - Deep Learning Classifying on the MNIST Dataset/342 - MNIST Preprocess the Data Create a Validation Set and Scale It English.srt 8.1 KB
62 - Appendix Additional Python Tools/470 - Iterating Over Range Objects English.srt 8.1 KB
4 - The Field of Data Science The Benefits of Each Discipline/10 - The Reason Behind These Disciplines English.srt 8.1 KB
18 - Statistics Inferential Statistics Confidence Intervals/110 - Margin of Error English.srt 8.0 KB
51 - Deep Learning Business Case Example/358 - Business Case Learning and Interpreting the Result English.srt 8.0 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/188 - How to Interpret the Regression Table English.srt 7.9 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 7.9 KB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/330 - Learning Rate Schedules or How to Choose the Optimal Learning Rate English.srt 7.9 KB
15 - Statistics Descriptive Statistics/87 - Standard Deviation and Coefficient of Variation English.srt 7.9 KB
38 - Advanced Statistical Methods KMeans Clustering/261 - To Standardize or not to Standardize English.srt 7.9 KB
56 - Software Integration/402 - What are Data Servers Clients Requests and Responses English.srt 7.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/113 - Confidence intervals Two means Independent Samples Part 1 English.srt 7.8 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/449 - Creating a Summary Table with the Coefficients and Intercept English.srt 7.8 KB
52 - Deep Learning Conclusion/366 - An overview of CNNs English.srt 7.8 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/211 - sklearn-Multiple-Linear-Regression.ipynb 7.8 KB
37 - Advanced Statistical Methods Cluster Analysis/250 - Some Examples of Clusters English.srt 7.8 KB
42 - Deep Learning Introduction to Neural Networks/283 - Introduction to Neural Networks English.srt 7.8 KB
11 - Probability Bayesian Inference/43 - Union of Sets English.srt 7.7 KB
40 - Part 6 Mathematics/274 - Arrays in Python A Convenient Way To Represent Matrices English.srt 7.7 KB
49 - Deep Learning Preprocessing/336 - Standardization English.srt 7.7 KB
39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps English.srt 7.7 KB
29 - Python Iterations/171 - While Loops and Incrementing English.srt 7.6 KB
36 - Advanced Statistical Methods Logistic Regression/247 - Testing-the-model-with-comments.ipynb 7.6 KB
23 - Python Variables and Data Types/145 - Strings-Lecture-Py3.ipynb 7.6 KB
25 - Python Other Python Operators/154 - Logical and Identity Operators English.srt 7.5 KB
10 - Probability Combinatorics/34 - Solving Combinations English.srt 7.5 KB
38 - Advanced Statistical Methods KMeans Clustering/258 - Selecting-the-number-of-clusters-with-comments.ipynb 7.5 KB
58 - Case Study Preprocessing the Absenteeismdata/419 - Analyzing the Reasons for Absence English.srt 7.5 KB
58 - Case Study Preprocessing the Absenteeismdata/440 - Working on Education Children and Pets English.srt 7.5 KB
15 - Statistics Descriptive Statistics/81 - Mean median and mode English.srt 7.5 KB
64 - Appendix Working with Text Files in Python/509 - Customer-Gender.csv 7.4 KB
11 - Probability Bayesian Inference/46 - The Conditional Probability Formula English.srt 7.4 KB
20 - Statistics Hypothesis Testing/127 - Test for the Mean Population Variance Unknown English.srt 7.4 KB
50 - Deep Learning Classifying on the MNIST Dataset/350 - MNIST Testing the Model English.srt 7.4 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/458 - Preparing the Deployment of the Model through a Module English.srt 7.4 KB
64 - Appendix Working with Text Files in Python/488 - An Introduction to Working with Files in Python English.srt 7.4 KB
38 - Advanced Statistical Methods KMeans Clustering/266 - Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.4 KB
36 - Advanced Statistical Methods Logistic Regression/234 - A Simple Example in Python English.srt 7.3 KB
17 - Statistics Inferential Statistics Fundamentals/96 - What is a Distribution English.srt 7.3 KB
58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.3 KB
64 - Appendix Working with Text Files in Python/495 - Common Naming Conventions English.srt 7.3 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/383 - 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.3 KB
56 - Software Integration/405 - Communication between Software Products through Text Files English.srt 7.3 KB
14 - Part 3 Statistics/70 - Population and Sample English.srt 7.3 KB
63 - Appendix pandas Fundamentals/480 - Using unique and nunique English.srt 7.3 KB
5 - The Field of Data Science Popular Data Science Techniques/13 - Techniques for Working with Big Data English.srt 7.3 KB
46 - Deep Learning Overfitting/318 - What is Overfitting English.srt 7.2 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/223 - sklearn-Train-Test-Split.ipynb 7.2 KB
15 - Statistics Descriptive Statistics/71 - Types of Data English.srt 7.2 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/455 - Saving the Model and Preparing it for Deployment English.srt 7.2 KB
52 - Deep Learning Conclusion/368 - An Overview of nonNN Approaches English.srt 7.2 KB
63 - Appendix pandas Fundamentals/479 - Parameters and Arguments in pandas English.srt 7.1 KB
12 - Probability Distributions/61 - Continuous Distributions The Standard Normal Distribution English.srt 7.1 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dummy-variables-with-comments.ipynb 7.1 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/198 - A2 No Endogeneity English.srt 7.0 KB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/332 - Adaptive Learning Rate Schedules AdaGrad and RMSprop English.srt 7.0 KB
18 - Statistics Inferential Statistics Confidence Intervals/106 - Confidence Interval Clarifications English.srt 7.0 KB
36 - Advanced Statistical Methods Logistic Regression/239 - Understanding Logistic Regression Tables English.srt 7.0 KB
40 - Part 6 Mathematics/278 - Transpose of a Matrix English.srt 6.9 KB
17 - Statistics Inferential Statistics Fundamentals/100 - Central Limit Theorem English.srt 6.9 KB
63 - Appendix pandas Fundamentals/487 - pandas DataFrames Indexing with loc English.srt 6.9 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/184 - Python Packages Installation English.srt 6.9 KB
64 - Appendix Working with Text Files in Python/506 - An Introduction to Working with Excel Files in Python English.srt 6.9 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/220 - Predicting with the Standardized Coefficients English.srt 6.9 KB
57 - Case Study Whats Next in the Course/407 - Game Plan for this Python SQL and Tableau Business Exercise English.srt 6.9 KB
63 - Appendix pandas Fundamentals/481 - Using sortvalues English.srt 6.9 KB
28 - Python Sequences/167 - List Slicing English.srt 6.8 KB
20 - Statistics Hypothesis Testing/123 - Type I Error and Type II Error English.srt 6.8 KB
44 - Deep Learning TensorFlow 20 Introduction/301 - TensorFlow Outline and Comparison with Other Libraries English.srt 6.8 KB
38 - Advanced Statistical Methods KMeans Clustering/264 - Market-segmentation-example-Part2-with-comments.ipynb 6.8 KB
8 - The Field of Data Science Debunking Common Misconceptions/24 - Debunking Common Misconceptions English.srt 6.8 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Minimal-example-Part-3.ipynb 6.8 KB
36 - Advanced Statistical Methods Logistic Regression/248 - Testing-the-Model-Exercise.ipynb 6.8 KB
20 - Statistics Hypothesis Testing/131 - Test for the mean Independent Samples Part 1 English.srt 6.8 KB
12 - Probability Distributions/56 - Discrete Distributions The Bernoulli Distribution English.srt 6.8 KB
42 - Deep Learning Introduction to Neural Networks/285 - Types of Machine Learning English.srt 6.8 KB
50 - Deep Learning Classifying on the MNIST Dataset/350 - TensorFlow-MNIST-complete.ipynb 6.8 KB
1 - Part 1 Introduction/2 - What Does the Course Cover English.srt 6.8 KB
49 - Deep Learning Preprocessing/338 - Binary and OneHot Encoding English.srt 6.8 KB
11 - Probability Bayesian Inference/40 - Sets and Events English.srt 6.8 KB
52 - Deep Learning Conclusion/363 - Summary on What Youve Learned English.srt 6.7 KB
64 - Appendix Working with Text Files in Python/514 - Saving Your Data with NumPy Part III csv English.srt 6.7 KB
20 - Statistics Hypothesis Testing/133 - Test for the mean Independent Samples Part 2 English.srt 6.7 KB
42 - Deep Learning Introduction to Neural Networks/292 - Common Objective Functions CrossEntropy Loss English.srt 6.7 KB
18 - Statistics Inferential Statistics Confidence Intervals/108 - Confidence Intervals Population Variance Unknown Tscore English.srt 6.7 KB
12 - Probability Distributions/65 - Continuous Distributions The Logistic Distribution English.srt 6.7 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Business Case A Comment on the Homework English.srt 6.6 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Backward Elimination or How to Simplify Your Model English.srt 6.6 KB
60 - Case Study Loading the absenteeismmodule/459 - absenteeism-module.py 6.6 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - Calculating the Accuracy of the Model English.srt 6.6 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/372 - TensorFlow Intro English.srt 6.6 KB
20 - Statistics Hypothesis Testing/126 - pvalue English.srt 6.5 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/451 - Standardizing only the Numerical Variables Creating a Custom Scaler English.srt 6.5 KB
36 - Advanced Statistical Methods Logistic Regression/242 - Binary Predictors in a Logistic Regression English.srt 6.5 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/313 - Activation Functions English.srt 6.5 KB
17 - Statistics Inferential Statistics Fundamentals/97 - The Normal Distribution English.srt 6.5 KB
58 - Case Study Preprocessing the Absenteeismdata/426 - Using concat in Python English.srt 6.4 KB
36 - Advanced Statistical Methods Logistic Regression/246 - Underfitting and Overfitting English.srt 6.4 KB
50 - Deep Learning Classifying on the MNIST Dataset/343 - TensorFlow-MNIST-Part2-with-comments.ipynb 6.4 KB
15 - Statistics Descriptive Statistics/89 - Covariance English.srt 6.4 KB
2 - The Field of Data Science The Various Data Science Disciplines/5 - What is the difference between Analysis and Analytics English.srt 6.4 KB
12 - Probability Distributions/60 - Continuous Distributions The Normal Distribution English.srt 6.3 KB
39 - Advanced Statistical Methods Other Types of Clustering/268 - Types of Clustering English.srt 6.3 KB
2 - The Field of Data Science The Various Data Science Disciplines/8 - A Breakdown of our Data Science Infographic English.srt 6.3 KB
36 - Advanced Statistical Methods Logistic Regression/235 - Logistic vs Logit Function English.srt 6.3 KB
64 - Appendix Working with Text Files in Python/490 - Structured SemiStructured and Unstructured Data English.srt 6.3 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/200 - A4 No Autocorrelation English.srt 6.2 KB
36 - Advanced Statistical Methods Logistic Regression/237 - Example-bank-data.csv 6.2 KB
46 - Deep Learning Overfitting/320 - What is Validation English.srt 6.2 KB
60 - Case Study Loading the absenteeismmodule/460 - Deploying the absenteeismmodule Part I English.srt 6.2 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.2 KB
15 - Statistics Descriptive Statistics/91 - Correlation Coefficient English.srt 6.2 KB
10 - Probability Combinatorics/33 - Solving Variations without Repetition English.srt 6.2 KB
28 - Python Sequences/169 - Dictionaries-Solution-Py3.ipynb 6.2 KB
37 - Advanced Statistical Methods Cluster Analysis/249 - Introduction to Cluster Analysis English.srt 6.1 KB
22 - Part 4 Introduction to Python/139 - Why Jupyter English.srt 6.1 KB
30 - Python Advanced Python Tools/179 - Importing Modules in Python English.srt 6.1 KB
54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.1 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/221 - sklearn-Feature-Scaling-Exercise.ipynb 6.1 KB
53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - Basic NN Example with TF Loss Function and Gradient Descent English.srt 6.1 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/207 - sklearn-Simple-Linear-Regression-with-comments.ipynb 6.1 KB
41 - Part 7 Deep Learning/282 - What to Expect from this Part English.srt 6.0 KB
48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/327 - Stochastic Gradient Descent English.srt 6.0 KB
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Exploring the Problem with a Machine Learning Mindset English.srt 6.0 KB
64 - Appendix Working with Text Files in Python/510 - Importing Files in Jupyter English.srt 6.0 KB
58 - Case Study Preprocessing the Absenteeismdata/437 - Extracting the Day of the Week from the Date Column English.srt 6.0 KB
23 - Python Variables and Data Types/143 - Variables English.srt 6.0 KB
64 - Appendix Working with Text Files in Python/511 - Saving Your Data with pandas English.srt 6.0 KB
64 - Appendix Working with Text Files in Python/515 - Saving-Data-NP-Exercise.ipynb 6.0 KB
42 - Deep Learning Introduction to Neural Networks/288 - The Linear model with Multiple Inputs and Multiple Outputs English.srt 6.0 KB
15 - Statistics Descriptive Statistics/72 - Levels of Measurement English.srt 6.0 KB
42 - Deep Learning Introduction to Neural Networks/284 - Training the Model English.srt 5.9 KB
38 - Advanced Statistical Methods KMeans Clustering/263 - Market-segmentation-example-with-comments.ipynb 5.9 KB
64 - Appendix Working with Text Files in Python/491 - Text Files and Data Connectivity English.srt 5.9 KB
25 - Python Other Python Operators/154 - Logical-and-Identity-Operators-Lecture-Py3.ipynb 5.9 KB
11 - Probability Bayesian Inference/49 - The Multiplication Law English.srt 5.9 KB
7 - The Field of Data Science Careers in Data Science/23 - Finding the Job What to Expect and What to Look for English.srt 5.8 KB
18 - Statistics Inferential Statistics Confidence Intervals/115 - Confidence intervals Two means Independent Samples Part 2 English.srt 5.8 KB
38 - Advanced Statistical Methods KMeans Clustering/254 - Country-clusters-with-comments.ipynb 5.8 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making-predictions.ipynb 5.8 KB
36 - Advanced Statistical Methods Logistic Regression/247 - Testing-the-model.ipynb 5.8 KB
51 - Deep Learning Business Case Example/356 - Business Case Load the Preprocessed Data English.srt 5.8 KB
40 - Part 6 Mathematics/271 - What is a Matrix English.srt 5.8 KB
64 - Appendix Working with Text Files in Python/509 - Importing Data with the squeeze Method English.srt 5.7 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/201 - A5 No Multicollinearity English.srt 5.7 KB
58 - Case Study Preprocessing the Absenteeismdata/439 - Analyzing Several Straightforward Columns for this Exercise English.srt 5.7 KB
38 - Advanced Statistical Methods KMeans Clustering/260 - Pros and Cons of KMeans Clustering English.srt 5.7 KB
11 - Probability Bayesian Inference/41 - Ways Sets Can Interact English.srt 5.7 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/315 - Backpropagation English.srt 5.7 KB
34 - Advanced Statistical Methods Linear Regression with sklearn/217 - sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.7 KB
27 - Python Python Functions/160 - How to Create a Function with a Parameter English.srt 5.7 KB
18 - Statistics Inferential Statistics Confidence Intervals/107 - Students T Distribution English.srt 5.7 KB
38 - Advanced Statistical Methods KMeans Clustering/256 - Categorical-data-with-comments.ipynb 5.6 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Basic NN Example Part 1 English.srt 5.6 KB
10 - Probability Combinatorics/35 - Symmetry of Combinations English.srt 5.6 KB
32 - Advanced Statistical Methods Linear Regression with StatsModels/189 - Decomposition of Variability English.srt 5.6 KB
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/314 - Activation Functions Softmax Activation English.srt 5.6 KB
51 - Deep Learning Business Case Example/354 - TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
35 - Advanced Statistical Methods Practical Example Linear Regression/227 - Practical Example Linear Regression Part 3 English.srt 5.6 KB
12 - Probability Distributions/64 - Continuous Distributions The Exponential Distribution English.srt 5.6 KB
37 - Advanced Statistical Methods Cluster Analysis/252 - Math Prerequisites English.srt 5.6 KB
38 - Advanced Statistical Methods KMeans Clustering/259 - How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.5 KB
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making Predictions with the Linear Regression English.srt 5.5 KB
27 - Python Python Functions/165 - Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.5 KB
15 - Statistics Descriptive Statistics/75 - Numerical Variables Frequency Distribution Table English.srt 5.5 KB
43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Basic NN Example Part 3 English.srt 5.5 KB
36 - Advanced Statistical Methods Logistic Regression/241 - What do the Odds Actually Mean English.srt 5.5 KB
10 - Probability Combinatorics/30 - Permutations and How to Use Them English.srt 5.5 KB
55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - The Importance of Working with a Balanced Dataset English.srt 5.5 KB
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54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/380 - MNIST How to Tackle the MNIST English.srt 4.8 KB
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58 - Case Study Preprocessing the Absenteeismdata/442 - A Note on Exporting Your Data as a csv File.html 883 bytes
58 - Case Study Preprocessing the Absenteeismdata/417 - EXERCISE Dropping a Column from a DataFrame in Python.html 870 bytes
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35 - Advanced Statistical Methods Practical Example Linear Regression/226 - A Note on Multicollinearity.html 849 bytes
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34 - Advanced Statistical Methods Linear Regression with sklearn/209 - A Note on Normalization.html 733 bytes
35 - Advanced Statistical Methods Practical Example Linear Regression/230 - Dummy Variables Exercise.html 713 bytes
53 - Appendix Deep Learning TensorFlow 1 Introduction/369 - READ ME.html 564 bytes
61 - Case Study Analyzing the Predicted Outputs in Tableau/467 - EXERCISE Transportation Expense vs Probability.html 553 bytes
45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/317 - Backpropagation A Peek into the Mathematics of Optimization.html 543 bytes
15 - Statistics Descriptive Statistics/86 - Variance Exercise.html 522 bytes
60 - Case Study Loading the absenteeismmodule/459 - Are You Sure Youre All Set.html 519 bytes
35 - Advanced Statistical Methods Practical Example Linear Regression/232 - Linear Regression Exercise.html 503 bytes
58 - Case Study Preprocessing the Absenteeismdata/431 - SOLUTION Reordering Columns in a Pandas DataFrame in Python.html 478 bytes
55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - Business Case Final Exercise.html 443 bytes
51 - Deep Learning Business Case Example/362 - Business Case Final Exercise.html 433 bytes
61 - Case Study Analyzing the Predicted Outputs in Tableau/465 - EXERCISE Reasons vs Probability.html 397 bytes
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61 - Case Study Analyzing the Predicted Outputs in Tableau/463 - EXERCISE Age vs Probability.html 385 bytes
34 - Advanced Statistical Methods Linear Regression with sklearn/215 - A Note on Calculation of Pvalues with sklearn.html 372 bytes
51 - Deep Learning Business Case Example/355 - Business Case Preprocessing the Data Exercise.html 370 bytes
36 - Advanced Statistical Methods Logistic Regression/247 - 2.03.Test-dataset.csv 322 bytes
64 - Appendix Working with Text Files in Python/504 - Importing Data with NumPy Exercise.html 308 bytes
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/457 - EXERCISE Saving the Model and Scaler.html 284 bytes
38 - Advanced Statistical Methods KMeans Clustering/263 - 3.12.Example.csv 283 bytes
64 - Appendix Working with Text Files in Python/515 - Saving Data with Numpy Exercise.html 260 bytes
39 - Advanced Statistical Methods Other Types of Clustering/270 - Country-clusters-standardized.csv 244 bytes
38 - Advanced Statistical Methods KMeans Clustering/254 - 3.01.Country-clusters.csv 200 bytes
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58 - Case Study Preprocessing the Absenteeismdata/427 - EXERCISE Using concat in Python.html 189 bytes
58 - Case Study Preprocessing the Absenteeismdata/430 - EXERCISE Reordering Columns in a Pandas DataFrame in Python.html 167 bytes
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Logistic Regression prior to Backward Elimination.txt 165 bytes
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58 - Case Study Preprocessing the Absenteeismdata/433 - EXERCISE Creating Checkpoints while Coding in Jupyter.html 137 bytes
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/457 - Logistic Regression.txt 135 bytes
58 - Case Study Preprocessing the Absenteeismdata/421 - EXERCISE Obtaining Dummies from a Single Feature.html 129 bytes
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58 - Case Study Preprocessing the Absenteeismdata/418 - SOLUTION Dropping a Column from a DataFrame in Python.html 114 bytes
36 - Advanced Statistical Methods Logistic Regression/237 - Building a Logistic Regression Exercise.html 87 bytes
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36 - Advanced Statistical Methods Logistic Regression/245 - Calculating the Accuracy of the Model.html 87 bytes
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38 - Advanced Statistical Methods KMeans Clustering/255 - A Simple Example of Clustering Exercise.html 87 bytes
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38 - Advanced Statistical Methods KMeans Clustering/259 - How to Choose the Number of Clusters Exercise.html 87 bytes
38 - Advanced Statistical Methods KMeans Clustering/266 - EXERCISE Species Segmentation with Cluster Analysis Part 1.html 87 bytes
38 - Advanced Statistical Methods KMeans Clustering/267 - EXERCISE Species Segmentation with Cluster Analysis Part 2.html 87 bytes
15 - Statistics Descriptive Statistics/74 - Categorical Variables Exercise.html 81 bytes
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15 - Statistics Descriptive Statistics/80 - Cross Tables and Scatter Plots Exercise.html 81 bytes
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15 - Statistics Descriptive Statistics/88 - Standard Deviation and Coefficient of Variation Exercise.html 81 bytes
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16 - Statistics Practical Example Descriptive Statistics/94 - Practical Example Descriptive Statistics Exercise.html 81 bytes
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18 - Statistics Inferential Statistics Confidence Intervals/105 - Confidence Intervals Population Variance Known Zscore Exercise.html 81 bytes
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20 - Statistics Hypothesis Testing/125 - Test for the Mean Population Variance Known Exercise.html 81 bytes
20 - Statistics Hypothesis Testing/128 - Test for the Mean Population Variance Unknown Exercise.html 81 bytes
20 - Statistics Hypothesis Testing/130 - Test for the Mean Dependent Samples Exercise.html 81 bytes
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21 - Statistics Practical Example Hypothesis Testing/136 - Practical Example Hypothesis Testing Exercise.html 81 bytes
50 - Deep Learning Classifying on the MNIST Dataset/343 - MNIST Preprocess the Data Scale the Test Data Exercise.html 79 bytes
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51 - Deep Learning Business Case Example/357 - Business Case Load the Preprocessed Data Exercise.html 79 bytes
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - Multiple Linear Regression Exercise.html 76 bytes
33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/203 - Dealing with Categorical Data Dummy Variables.html 76 bytes
34 - Advanced Statistical Methods Linear Regression with sklearn/210 - Simple Linear Regression with sklearn Exercise.html 76 bytes
34 - Advanced Statistical Methods Linear Regression with sklearn/213 - Calculating the Adjusted RSquared in sklearn Exercise.html 76 bytes
34 - Advanced Statistical Methods Linear Regression with sklearn/217 - Multiple Linear Regression Exercise.html 76 bytes
34 - Advanced Statistical Methods Linear Regression with sklearn/221 - Feature Scaling Standardization Exercise.html 76 bytes
35 - Advanced Statistical Methods Practical Example Linear Regression/228 - Dummies and Variance Inflation Factor Exercise.html 76 bytes
1 - Part 1 Introduction/3 - Download all resources.txt 73 bytes
35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn Linear Regression Practical Example Part 3.txt 73 bytes
64 - Appendix Working with Text Files in Python/488 - Section Resources Working with Text Files.txt 73 bytes
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64 - Appendix Working with Text Files in Python/496 - source.txt 39 bytes
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