[FreeAllCourse.Com] Udemy- The Complete Machine Learning Course with Python

Creation Time	Jan. 20, 2026, 1:54 p.m.
Last Access Time	Jan. 20, 2026, 5:56 p.m.
File Size	6.8 GB
Keywords	Complete Python with FreeAllCourse Machine Course Udemy- Learning The Com
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Total Files	221
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6. Tree/6. Project HR.mp4 177.8 MB
7. Ensemble Machine Learning/2. Bagging.mp4 165.4 MB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 155.6 MB
3. Regression/2. EDA.mp4 151.7 MB
11. Deep Learning/3. Motivational Example - Project MNIST.mp4 145.0 MB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 143.9 MB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 141.9 MB
3. Regression/15. Data Preprocessing.mp4 135.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 128.5 MB
3. Regression/19. CV Illustration.mp4 127.2 MB
10. Unsupervised Learning Clustering/1. Clustering.mp4 125.7 MB
3. Regression/9. Multiple Regression 1.mp4 125.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 124.9 MB
4. Classification/1. Logistic Regression.mp4 119.6 MB
3. Regression/7. Robust Regression.mp4 119.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 111.1 MB
3. Regression/12. Polynomial Regression.mp4 110.8 MB
4. Classification/3. Understanding MNIST.mp4 109.0 MB
3. Regression/4. Correlation Analysis and Feature Selection.mp4 105.2 MB
4. Classification/10. Precision Recall Tradeoff.mp4 102.0 MB
3. Regression/8. Evaluate Regression Model Performance.mp4 99.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 97.0 MB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 93.5 MB
3. Regression/10. Multiple Regression 2.mp4 91.2 MB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 89.8 MB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 88.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 88.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 84.4 MB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 80.9 MB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 80.3 MB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 79.8 MB
3. Regression/6. Five Steps Machine Learning Process.mp4 77.3 MB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 77.2 MB
3. Regression/5. Linear Regression with Scikit-Learn.mp4 77.0 MB
11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 76.0 MB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 75.7 MB
11. Deep Learning/4. Binary Classification Problem.mp4 72.1 MB
5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 70.1 MB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 70.1 MB
3. Regression/16. Variance-Bias Trade Off.mp4 68.7 MB
6. Tree/7. Project HR with Google Colab.mp4 66.6 MB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 66.2 MB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 64.6 MB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 63.7 MB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 62.9 MB
3. Regression/13. Dealing with Non-linear Relationships.mp4 62.7 MB
5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 59.7 MB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 59.2 MB
10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 57.7 MB
4. Classification/4. SGD.mp4 57.3 MB
3. Regression/17. Learning Curve.mp4 56.4 MB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 55.9 MB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 55.0 MB
6. Tree/3. Visualizing Boundary.mp4 54.7 MB
4. Classification/6. Confusion Matrix.mp4 54.7 MB
1. Introduction/1. What Does the Course Cover.mp4 54.4 MB
4. Classification/12. ROC.mp4 52.2 MB
4. Classification/5. Performance Measure and Stratified k-Fold.mp4 51.5 MB
6. Tree/2. Training and Visualizing a Decision Tree.mp4 51.4 MB
2. Getting Started with Anaconda/2. Hello World.mp4 51.2 MB
7. Ensemble Machine Learning/4. AdaBoost.mp4 49.8 MB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 49.4 MB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 49.0 MB
3. Regression/1. Scikit-Learn.mp4 48.5 MB
3. Regression/18. Cross Validation.mp4 48.0 MB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 47.9 MB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 46.4 MB
3. Regression/11. Regularized Regression.mp4 44.3 MB
6. Tree/1. Introduction to Decision Tree.mp4 43.9 MB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 43.8 MB
4. Classification/2. Introduction to Classification.mp4 42.1 MB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 40.6 MB
6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 40.1 MB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 38.4 MB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 37.9 MB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 37.8 MB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 37.5 MB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 37.2 MB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 36.6 MB
3. Regression/14. Feature Importance.mp4 36.3 MB
6. Tree/5. End to End Modeling.mp4 35.6 MB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 35.4 MB
7. Ensemble Machine Learning/7. XGBoost.mp4 35.1 MB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 35.0 MB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 34.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 32.3 MB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 31.4 MB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 30.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 30.0 MB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 29.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 28.5 MB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 27.4 MB
4. Classification/7. Precision.mp4 23.6 MB
3. Regression/3. Correlation Analysis and Feature Selection.mp4 22.6 MB
11. Deep Learning/2. Neural Network Architecture.mp4 22.4 MB
7. Ensemble Machine Learning/6. XGBoost Installation.mp4 22.3 MB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 22.0 MB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 21.4 MB
4. Classification/11. Altering the Precision Recall Tradeoff.mp4 20.9 MB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 20.9 MB
4. Classification/8. Recall.mp4 19.6 MB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 18.7 MB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 16.9 MB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14.2 MB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 13.8 MB
4. Classification/9. f1.mp4 12.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11.2 MB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9.1 MB
3. Regression/3.1 0305.zip.zip 2.1 MB
8. k-Nearest Neighbours (kNN)/4.1 0805.zip.zip 40.8 KB
6. Tree/6. Project HR.vtt 28.1 KB
3. Regression/15. Data Preprocessing.vtt 25.5 KB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.vtt 24.4 KB
11. Deep Learning/3. Motivational Example - Project MNIST.vtt 23.5 KB
4. Classification/1. Logistic Regression.vtt 23.5 KB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.vtt 23.1 KB
3. Regression/9. Multiple Regression 1.vtt 22.5 KB
3. Regression/2. EDA.vtt 22.4 KB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.vtt 22.4 KB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.vtt 22.1 KB
7. Ensemble Machine Learning/2. Bagging.vtt 21.1 KB
4. Classification/10. Precision Recall Tradeoff.vtt 20.8 KB
3. Regression/7. Robust Regression.vtt 20.1 KB
3. Regression/19. CV Illustration.vtt 19.9 KB
3. Regression/12. Polynomial Regression.vtt 19.7 KB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.vtt 18.9 KB
10. Unsupervised Learning Clustering/1. Clustering.vtt 18.7 KB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.vtt 18.5 KB
3. Regression/8. Evaluate Regression Model Performance.vtt 17.9 KB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.vtt 16.7 KB
4. Classification/3. Understanding MNIST.vtt 16.4 KB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.vtt 15.4 KB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.vtt 15.2 KB
3. Regression/5. Linear Regression with Scikit-Learn.vtt 14.9 KB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.vtt 14.5 KB
3. Regression/4. Correlation Analysis and Feature Selection.vtt 13.9 KB
3. Regression/10. Multiple Regression 2.vtt 13.8 KB
3. Regression/16. Variance-Bias Trade Off.vtt 13.7 KB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.vtt 12.9 KB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.vtt 12.6 KB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.vtt 12.5 KB
2. Getting Started with Anaconda/2. Hello World.vtt 12.5 KB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.vtt 12.1 KB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.vtt 12.1 KB
11. Deep Learning/5. Natural Language Processing - Binary Classification.vtt 11.7 KB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.vtt 11.5 KB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.vtt 11.5 KB
11. Deep Learning/4. Binary Classification Problem.vtt 11.5 KB
6. Tree/7. Project HR with Google Colab.vtt 11.4 KB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.vtt 11.4 KB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.vtt 11.2 KB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.vtt 11.1 KB
4. Classification/6. Confusion Matrix.vtt 11.0 KB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.vtt 11.0 KB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.vtt 10.7 KB
4. Classification/4. SGD.vtt 10.6 KB
3. Regression/13. Dealing with Non-linear Relationships.vtt 10.3 KB
3. Regression/17. Learning Curve.vtt 10.2 KB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.vtt 10.0 KB
10. Unsupervised Learning Clustering/2. k_Means Clustering.vtt 10.0 KB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.vtt 10.0 KB
3. Regression/1. Scikit-Learn.vtt 10.0 KB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.vtt 9.9 KB
3. Regression/3. Correlation Analysis and Feature Selection.vtt 9.8 KB
3. Regression/18. Cross Validation.vtt 9.7 KB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.vtt 9.5 KB
5. Support Vector Machine (SVM)/5. Support Vector Regression.vtt 9.3 KB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.vtt 9.2 KB
3. Regression/6. Five Steps Machine Learning Process.vtt 9.2 KB
6. Tree/3. Visualizing Boundary.vtt 8.8 KB
5. Support Vector Machine (SVM)/4. Radial Basis Function.vtt 8.8 KB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.vtt 8.7 KB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.vtt 8.2 KB
4. Classification/5. Performance Measure and Stratified k-Fold.vtt 8.1 KB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.vtt 8.0 KB
6. Tree/1. Introduction to Decision Tree.vtt 7.9 KB
7. Ensemble Machine Learning/4. AdaBoost.vtt 7.9 KB
3. Regression/11. Regularized Regression.vtt 7.8 KB
4. Classification/12. ROC.vtt 7.6 KB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.vtt 7.3 KB
11. Deep Learning/2. Neural Network Architecture.vtt 7.2 KB
6. Tree/2. Training and Visualizing a Decision Tree.vtt 7.0 KB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.vtt 7.0 KB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.vtt 6.9 KB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.vtt 6.7 KB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.vtt 6.2 KB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.vtt 6.1 KB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.vtt 6.0 KB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.vtt 6.0 KB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.vtt 5.9 KB
4. Classification/2. Introduction to Classification.vtt 5.7 KB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.vtt 5.7 KB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.vtt 5.6 KB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.vtt 5.5 KB
3. Regression/14. Feature Importance.vtt 5.4 KB
6. Tree/5. End to End Modeling.vtt 5.3 KB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.vtt 5.3 KB
6. Tree/4. Tree Regression, Regularization and Over Fitting.vtt 5.3 KB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.vtt 5.3 KB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.vtt 5.1 KB
7. Ensemble Machine Learning/7. XGBoost.vtt 5.1 KB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.vtt 5.1 KB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.vtt 4.9 KB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.vtt 4.3 KB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.vtt 4.3 KB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.vtt 4.1 KB
4. Classification/7. Precision.vtt 4.1 KB
4. Classification/8. Recall.vtt 3.7 KB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.vtt 3.6 KB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.vtt 3.6 KB
4. Classification/11. Altering the Precision Recall Tradeoff.vtt 3.5 KB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.vtt 3.3 KB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.vtt 3.0 KB
1. Introduction/1. What Does the Course Cover.vtt 3.0 KB
7. Ensemble Machine Learning/6. XGBoost Installation.vtt 2.8 KB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.vtt 2.5 KB
4. Classification/9. f1.vtt 2.3 KB
1. Introduction/2. How to Succeed in This Course.html 2.2 KB
1. Introduction/3. Project Files and Resources.html 1.7 KB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.vtt 1.6 KB
8. k-Nearest Neighbours (kNN)/3. Addition Materials.html 335 bytes

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