[FreeCourseSite.com] Udemy - Machine Learning Essentials (2023) - Master core ML concepts

Creation Time	Sept. 13, 2024, 2:59 a.m.
Last Access Time	March 1, 2025, 2:47 p.m.
File Size	15.9 GB
Keywords	core Essentials FreeCourseSite ML concepts - Udemy Machine Master Learning 2023 com
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Total Files	214
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5. Logistic Regression/3. Hypothesis Function.mp4 272.3 MB
3. Linear Regression/7. Gradient Descent Code.mp4 271.3 MB
19. Ensemble Learning Boosting/5. GBDT Algorithm.mp4 245.2 MB
4. Linear Regression - Multiple Features/8. Code 04 - Gradient Computation.mp4 222.3 MB
12. Naive Bayes Algorithm/7. Understanding Golf Dataset.mp4 218.7 MB
19. Ensemble Learning Boosting/3. Boosting Mathematical Formulation.mp4 211.5 MB
13. Multinomial Naive Bayes/4. Bernoulli Naive Bayes.mp4 204.7 MB
15. Decision Trees/5. Information Gain.mp4 199.5 MB
2. Supervised vs Unsupervised Learning/2. Supervised Learning Example.mp4 198.1 MB
9. PROJECT - Face Recognition/7. Face Recognition 01 - Data Collection.mp4 198.0 MB
3. Linear Regression/4. Loss Error Function.mp4 195.4 MB
12. Naive Bayes Algorithm/6. Computing Likelihood.mp4 193.2 MB
13. Multinomial Naive Bayes/3. Multinomial Naive Bayes Example.mp4 179.2 MB
7. Principal Component Analysis (PCA)/3. Maximising Variance.mp4 178.0 MB
3. Linear Regression/2. Notation.mp4 171.4 MB
3. Linear Regression/11. Code 02 - Data Normalisation.mp4 170.9 MB
12. Naive Bayes Algorithm/10. CODE - Likelihood.mp4 166.5 MB
12. Naive Bayes Algorithm/5. Naive Bayes for Text Classification.mp4 160.7 MB
14. PROJECT Spam Classifier/2. Data Clearning.mp4 157.9 MB
19. Ensemble Learning Boosting/4. Concept of Pseudo Residuals.mp4 152.8 MB
5. Logistic Regression/5. Gradient Update Rule.mp4 146.6 MB
12. Naive Bayes Algorithm/3. Bayes Theorem Question.mp4 145.0 MB
18. Ensemble Learning Bagging/3. Why Bagging Helps.mp4 142.6 MB
13. Multinomial Naive Bayes/1. Multinomial Naive Bayes.mp4 141.1 MB
7. Principal Component Analysis (PCA)/2. Conceptual Overview of PCA.mp4 140.9 MB
3. Linear Regression/15. R2 Score.mp4 139.3 MB
13. Multinomial Naive Bayes/5. Bernoulli Naive Bayes Example.mp4 138.3 MB
15. Decision Trees/2. Decision Trees Example.mp4 137.4 MB
15. Decision Trees/6. CODE Split Data.mp4 135.8 MB
19. Ensemble Learning Boosting/2. Boosting Intuition.mp4 133.5 MB
19. Ensemble Learning Boosting/7. CODE - Gradient Boosting Decision Trees.mp4 131.6 MB
18. Ensemble Learning Bagging/2. Bagging Model.mp4 128.8 MB
18. Ensemble Learning Bagging/5. Bias Variance Tradeoff.mp4 127.4 MB
20. PROJECT Customer Churn Prediction/1. Project Overview.mp4 122.4 MB
19. Ensemble Learning Boosting/1. Boosting Introduction.mp4 120.4 MB
16. Decision Trees Implementation/2. CODE - Train Decision Tree.mp4 119.7 MB
19. Ensemble Learning Boosting/8. XGBoost.mp4 119.3 MB
19. Ensemble Learning Boosting/9. Adaptive Boosting (AdaBoost).mp4 118.9 MB
15. Decision Trees/3. Entropy.mp4 118.4 MB
3. Linear Regression/13. Code 04 - Modelling.mp4 118.1 MB
18. Ensemble Learning Bagging/4. Random Forest Algorithm.mp4 118.1 MB
16. Decision Trees Implementation/7. CODE - Prediction.mp4 116.4 MB
18. Ensemble Learning Bagging/6. CODE Random Forest.mp4 115.6 MB
12. Naive Bayes Algorithm/12. Implementing Naive Bayes - Sklearn.mp4 111.5 MB
3. Linear Regression/6. Gradient Descent Optimisation.mp4 110.4 MB
16. Decision Trees Implementation/8. Handling Numeric Features.mp4 110.0 MB
13. Multinomial Naive Bayes/7. Gaussian Naive Bayes.mp4 109.3 MB
12. Naive Bayes Algorithm/9. CODE - Conditional Probability.mp4 108.1 MB
14. PROJECT Spam Classifier/3. WordCloud.mp4 106.2 MB
5. Logistic Regression/2. Notation.mp4 105.3 MB
3. Linear Regression/9. The Math of Training.mp4 105.3 MB
4. Linear Regression - Multiple Features/5. Code 01 - Data Prep.mp4 104.3 MB
3. Linear Regression/17. Code 07 - Visualisation.mp4 103.4 MB
20. PROJECT Customer Churn Prediction/2. Exploratory Data Analysis.mp4 103.3 MB
16. Decision Trees Implementation/6. CODE - Explore Decision Tree Model.mp4 102.3 MB
20. PROJECT Customer Churn Prediction/7. Hyperparameter tuning.mp4 101.2 MB
17. PROJECT Titanic Survival Prediction/1. Project Overview.mp4 100.8 MB
1. Introduction/7. Automatic Speech Recognition.mp4 100.7 MB
9. PROJECT - Face Recognition/9. Face Recognition 03 - Predictions using KNN.mp4 99.6 MB
15. Decision Trees/9. Stopping Conditions.mp4 98.3 MB
7. Principal Component Analysis (PCA)/4. Minimising Distances.mp4 95.3 MB
3. Linear Regression/3. Hypothesis.mp4 95.1 MB
17. PROJECT Titanic Survival Prediction/5. Handling Missing Values.mp4 94.8 MB
13. Multinomial Naive Bayes/6. Bias Variance Tradeoff.mp4 94.4 MB
2. Supervised vs Unsupervised Learning/3. Unsupervised Learning.mp4 94.0 MB
3. Linear Regression/18. Code 08 - Trajectory [Optional].mp4 93.9 MB
13. Multinomial Naive Bayes/8. CODE - Variants of Naive Bayes.mp4 93.9 MB
15. Decision Trees/7. CODE Information Gain.mp4 93.8 MB
17. PROJECT Titanic Survival Prediction/7. Visualize Decision Tree.mp4 92.6 MB
13. Multinomial Naive Bayes/2. Laplace Smoothing.mp4 91.5 MB
5. Logistic Regression/4. Binary Cross-Entropy Loss Function.mp4 90.8 MB
8. K-Nearest Neigbours/4. KNN Algorithm Code.mp4 90.8 MB
16. Decision Trees Implementation/10. Decision Trees for Regression.mp4 89.5 MB
3. Linear Regression/12. Code 03 - Train Test Split.mp4 89.3 MB
21. Deep Learning Introduction - Neural Network/8. Tensorflow Playground.mp4 88.7 MB
4. Linear Regression - Multiple Features/1. Introduction.mp4 88.2 MB
14. PROJECT Spam Classifier/1. Project Overview.mp4 87.4 MB
12. Naive Bayes Algorithm/1. Bayes Theorem.mp4 87.3 MB
4. Linear Regression - Multiple Features/9. Code 05 - Training Loop.mp4 86.7 MB
5. Logistic Regression/1. Binary Classification Introduction.mp4 85.5 MB
21. Deep Learning Introduction - Neural Network/11. CODE - Model Training and Testing.mp4 85.0 MB
17. PROJECT Titanic Survival Prediction/2. Exploratory Data Analysis.mp4 83.8 MB
16. Decision Trees Implementation/5. CODE - Train Child Nodes.mp4 83.4 MB
19. Ensemble Learning Boosting/6. Bias Variance Tradeoff.mp4 83.4 MB
17. PROJECT Titanic Survival Prediction/4. Data Preparation for ML Model.mp4 83.4 MB
10. K-Means/6. Code 05 - Visualizing K-Means & Results.mp4 81.8 MB
12. Naive Bayes Algorithm/4. Naive Bayes Algorithm.mp4 80.7 MB
5. Logistic Regression/6. Code 01 - Data Prep.mp4 79.9 MB
9. PROJECT - Face Recognition/3. Object Detection using Haarcascades.mp4 79.6 MB
17. PROJECT Titanic Survival Prediction/3. Exploratory Data Analysis - II.mp4 79.0 MB
9. PROJECT - Face Recognition/4. Face Detection in Images.mp4 78.7 MB
4. Linear Regression - Multiple Features/6. Code 02 - Hypothesis.mp4 78.5 MB
2. Supervised vs Unsupervised Learning/1. Supervised Learning Introduction.mp4 78.3 MB
15. Decision Trees/1. Decision Trees Introduction.mp4 78.0 MB
17. PROJECT Titanic Survival Prediction/6. Decision Tree Model Building.mp4 77.8 MB
10. K-Means/4. Code 03 - Assigning Points.mp4 75.6 MB
12. Naive Bayes Algorithm/2. Derivation of Bayes Theorem.mp4 74.8 MB
20. PROJECT Customer Churn Prediction/6. Model Building.mp4 74.6 MB
5. Logistic Regression/14. Multiclass Classification One Vs Rest.mp4 72.4 MB
16. Decision Trees Implementation/4. CODE - Stopping Conditions.mp4 72.4 MB
9. PROJECT - Face Recognition/8. Face Recognition 02 - Loading Data.mp4 71.7 MB
12. Naive Bayes Algorithm/11. CODE - Prediction.mp4 71.4 MB
11. Project - Dominant Color Extraction/5. Image in K-Colors.mp4 71.0 MB
15. Decision Trees/4. CODE Entropy.mp4 70.1 MB
18. Ensemble Learning Bagging/1. Ensemble Learning.mp4 69.3 MB
3. Linear Regression/10. Code 01 - Data Generation.mp4 68.2 MB
14. PROJECT Spam Classifier/6. Model Evaluation.mp4 67.9 MB
20. PROJECT Customer Churn Prediction/4. Finding relations.mp4 67.5 MB
1. Introduction/3. Machine Learning.mp4 67.0 MB
15. Decision Trees/8. Construction of Decision Trees.mp4 66.4 MB
10. K-Means/3. Code 02 - Init Centers.mp4 65.7 MB
1. Introduction/6. Natural Language Processing.mp4 64.4 MB
6. Dimensionality Reduction Feature Selection/6. Feature Selection - Code.mp4 63.6 MB
7. Principal Component Analysis (PCA)/1. Introduction to PCA.mp4 63.4 MB
5. Logistic Regression/10. Code 05 - Training Loop.mp4 61.6 MB
20. PROJECT Customer Churn Prediction/5. Data Preparation.mp4 61.3 MB
16. Decision Trees Implementation/1. CODE - Decision Tree Node.mp4 61.2 MB
12. Naive Bayes Algorithm/8. CODE - Prior Probability.mp4 61.1 MB
10. K-Means/1. K-Means Algorithm.mp4 60.1 MB
16. Decision Trees Implementation/3. CODE - Assign Target Variable to Each Node.mp4 59.9 MB
10. K-Means/5. Code 04 - Updating Centroids.mp4 59.1 MB
16. Decision Trees Implementation/9. Bias Variance Tradeoff.mp4 58.9 MB
21. Deep Learning Introduction - Neural Network/5. Neural Networks.mp4 58.0 MB
5. Logistic Regression/12. Code 07 - Predictions & Accuracy.mp4 55.5 MB
1. Introduction/4. Deep Learning.mp4 54.5 MB
3. Linear Regression/14. Code 05 - Predictions.mp4 54.1 MB
11. Project - Dominant Color Extraction/3. Finding Clusters.mp4 53.9 MB
8. K-Nearest Neigbours/8. KNN Pros and Cons.mp4 53.8 MB
21. Deep Learning Introduction - Neural Network/4. Gradient Descent Updates.mp4 52.8 MB
20. PROJECT Customer Churn Prediction/3. Data Visualisation.mp4 52.5 MB
14. PROJECT Spam Classifier/5. Model Building.mp4 52.1 MB
3. Linear Regression/8. Gradient Descent - for Linear Regression.mp4 51.8 MB
4. Linear Regression - Multiple Features/11. Code 06 - Evaluation.mp4 50.9 MB
7. Principal Component Analysis (PCA)/8. PCA Code.mp4 50.6 MB
22. PROJECT Pokemon Image Classification/5. Data Preprocessing.mp4 50.3 MB
22. PROJECT Pokemon Image Classification/9. Model evaluation.mp4 50.2 MB
21. Deep Learning Introduction - Neural Network/7. Why Neural Nets.mp4 49.9 MB
1. Introduction/1. Course Overview.mp4 49.6 MB
9. PROJECT - Face Recognition/5. Face Detection in Live Video.mp4 49.3 MB
22. PROJECT Pokemon Image Classification/2. The Data.mp4 48.6 MB
1. Introduction/2. Artificial Intelligence.mp4 48.6 MB
7. Principal Component Analysis (PCA)/5. Eigen Values & Eigen Vectors.mp4 48.5 MB
3. Linear Regression/5. Training Idea.mp4 48.3 MB
21. Deep Learning Introduction - Neural Network/10. CODE - Model Building.mp4 45.8 MB
7. Principal Component Analysis (PCA)/9. Choosing the right dimensions.mp4 45.4 MB
5. Logistic Regression/9. Code 04 - Gradient Computation.mp4 45.2 MB
8. K-Nearest Neigbours/1. Introduction.mp4 45.0 MB
7. Principal Component Analysis (PCA)/7. Understanding Eigen Values.mp4 44.6 MB
14. PROJECT Spam Classifier/4. Text Featurization.mp4 44.2 MB
1. Introduction/8. Reinforcement Learning.mp4 43.9 MB
21. Deep Learning Introduction - Neural Network/9. CODE -Data Preparation.mp4 43.8 MB
4. Linear Regression - Multiple Features/4. Training & Gradient Updates.mp4 43.3 MB
5. Logistic Regression/11. Code 06 - Visualise Decision Boundary.mp4 43.1 MB
1. Introduction/5. Computer Vision.mp4 43.1 MB
22. PROJECT Pokemon Image Classification/4. Data Loading.mp4 42.8 MB
21. Deep Learning Introduction - Neural Network/3. How does a perceptron Learns.mp4 42.8 MB
11. Project - Dominant Color Extraction/4. Dominant Color Swatches.mp4 39.7 MB
16. Decision Trees Implementation/11. Decision Tree Code - Sklearn.mp4 36.7 MB
22. PROJECT Pokemon Image Classification/1. Introduction.mp4 35.8 MB
4. Linear Regression - Multiple Features/12. Linear Regression using Sk-Learn.mp4 35.4 MB
8. K-Nearest Neigbours/2. KNN Idea.mp4 34.5 MB
9. PROJECT - Face Recognition/2. OpenCV - Video Input from WebCam.mp4 34.2 MB
5. Logistic Regression/7. Code 02 - Hypothesis Logit Model.mp4 34.1 MB
21. Deep Learning Introduction - Neural Network/2. A Neuron.mp4 34.1 MB
9. PROJECT - Face Recognition/1. OpenCV - Working with Images.mp4 34.0 MB
5. Logistic Regression/15. Multiclass Classification One Vs One.mp4 33.5 MB
22. PROJECT Pokemon Image Classification/6. Model Architecture.mp4 33.2 MB
4. Linear Regression - Multiple Features/3. Loss Function.mp4 33.2 MB
22. PROJECT Pokemon Image Classification/3. Structured Data.mp4 31.9 MB
22. PROJECT Pokemon Image Classification/10. Predictions.mp4 30.2 MB
4. Linear Regression - Multiple Features/10. A Note about Shapes.mp4 30.1 MB
5. Logistic Regression/13. Logistic Regression using Sk-Learn.mp4 29.5 MB
8. K-Nearest Neigbours/3. KNN Data Prep.mp4 29.2 MB
3. Linear Regression/16. Code 06 - Evaluation.mp4 28.8 MB
4. Linear Regression - Multiple Features/2. Hypothesis.mp4 28.8 MB
21. Deep Learning Introduction - Neural Network/1. Biological Neural Network.mp4 28.4 MB
21. Deep Learning Introduction - Neural Network/6. 3 Layer NN.mp4 28.0 MB
3. Linear Regression/1. Introduction to Linear Regression.mp4 26.6 MB
11. Project - Dominant Color Extraction/1. Introduction.mp4 25.1 MB
11. Project - Dominant Color Extraction/2. Reading Images.mp4 24.2 MB
6. Dimensionality Reduction Feature Selection/3. Filter Method.mp4 23.5 MB
6. Dimensionality Reduction Feature Selection/4. Wrapper Method.mp4 23.0 MB
4. Linear Regression - Multiple Features/7. Code 03 - Loss Function.mp4 22.5 MB
5. Logistic Regression/8. Code 03 - Binary Cross Entropy Loss.mp4 19.4 MB
10. K-Means/2. Code 01 - Data Prep.mp4 18.6 MB
22. PROJECT Pokemon Image Classification/7. Softmax Function.mp4 18.4 MB
7. Principal Component Analysis (PCA)/6. PCA Summary.mp4 18.3 MB
22. PROJECT Pokemon Image Classification/8. Model Training.mp4 17.3 MB
6. Dimensionality Reduction Feature Selection/1. Curse of Dimensionality.mp4 17.0 MB
8. K-Nearest Neigbours/7. KNN and Data Standardisation.mp4 15.2 MB
9. PROJECT - Face Recognition/6. Face Recognition Project Intro.mp4 15.2 MB
6. Dimensionality Reduction Feature Selection/2. Feature Selection Vs. Feature Extraction.mp4 15.1 MB
8. K-Nearest Neigbours/5. Euclidean and Manhattan Distance.mp4 14.9 MB
6. Dimensionality Reduction Feature Selection/5. Embedded Method.mp4 12.8 MB
8. K-Nearest Neigbours/6. Deciding value of K.mp4 6.8 MB
6. Dimensionality Reduction Feature Selection/6.1 train.csv 119.5 KB
17. PROJECT Titanic Survival Prediction/1.1 titanic_train.csv 58.9 KB
1. Introduction/9. Pre-requisites.html 889 bytes
12. Naive Bayes Algorithm/7.1 golf.csv 430 bytes
8. K-Nearest Neigbours/9. KNN using Sk-Learn.html 405 bytes
1. Introduction/10. Code Repository.html 236 bytes
22. PROJECT Pokemon Image Classification/1.1 Dataset Link.html 129 bytes
0. Websites you may like/[FreeCourseSite.com].url 127 bytes
10. K-Means/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
15. Decision Trees/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
4. Linear Regression - Multiple Features/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
0. Websites you may like/[CourseClub.Me].url 122 bytes
10. K-Means/0. Websites you may like/[CourseClub.Me].url 122 bytes
15. Decision Trees/0. Websites you may like/[CourseClub.Me].url 122 bytes
4. Linear Regression - Multiple Features/0. Websites you may like/[CourseClub.Me].url 122 bytes
0. Websites you may like/[GigaCourse.Com].url 49 bytes
10. K-Means/0. Websites you may like/[GigaCourse.Com].url 49 bytes
15. Decision Trees/0. Websites you may like/[GigaCourse.Com].url 49 bytes
4. Linear Regression - Multiple Features/0. Websites you may like/[GigaCourse.Com].url 49 bytes

[FreeCourseSite.com] Udemy - Machine Learning Essentials (2023) - Master core ML concepts

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