Udemy - The Complete Machine Learning Course with Python [Desire ...

Files:

• 1. What Does the Course Cover.mp4 (54.4 MB)
• 1. What Does the Course Cover.vtt (3.0 KB)
• 2. How to Succeed in This Course.html (2.2 KB)
• 3. Project Files and Resources.html (1.7 KB)

• 1. Clustering.mp4 (125.7 MB)
• 1. Clustering.vtt (18.7 KB)
• 2. k_Means Clustering.mp4 (57.7 MB)
• 2. k_Means Clustering.vtt (10.0 KB)

• 1. Estimating Simple Function with Neural Networks.mp4 (143.9 MB)
• 1. Estimating Simple Function with Neural Networks.vtt (24.4 KB)
• 2. Neural Network Architecture.mp4 (22.4 MB)
• 2. Neural Network Architecture.vtt (7.2 KB)
• 3. Motivational Example - Project MNIST.mp4 (145.0 MB)
• 3. Motivational Example - Project MNIST.vtt (23.5 KB)
• 4. Binary Classification Problem.mp4 (72.1 MB)
• 4. Binary Classification Problem.vtt (11.5 KB)
• 5. Natural Language Processing - Binary Classification.mp4 (76.0 MB)
• 5. Natural Language Processing - Binary Classification.vtt (11.7 KB)

• 1. Introduction to Neural Networks.mp4 (13.8 MB)
• 1. Introduction to Neural Networks.vtt (2.5 KB)
• 10. Gradient Based Optimization.mp4 (55.0 MB)
• 10. Gradient Based Optimization.vtt (12.6 KB)
• 11. Getting Started with Neural Network and Deep Learning Libraries.mp4 (18.7 MB)
• 11. Getting Started with Neural Network and Deep Learning Libraries.vtt (5.1 KB)
• 12. Categories of Machine Learning.mp4 (37.5 MB)
• 12. Categories of Machine Learning.vtt (11.2 KB)
• 13. Over and Under Fitting.mp4 (70.1 MB)
• 13. Over and Under Fitting.vtt (16.7 KB)
• 14. Machine Learning Workflow.mp4 (27.4 MB)
• 14. Machine Learning Workflow.vtt (5.3 KB)
• 2. Differences between Classical Programming and Machine Learning.mp4 (20.9 MB)
• 2. Differences between Classical Programming and Machine Learning.vtt (4.9 KB)
• 3. Learning Representations.mp4 (77.2 MB)
• 3. Learning Representations.vtt (11.5 KB)
• 4. What is Deep Learning.mp4 (155.6 MB)
• 4. What is Deep Learning.vtt (23.1 KB)
• 5. Learning Neural Networks.mp4 (40.6 MB)
• 5. Learning Neural Networks.vtt (11.4 KB)
• 6. Why Now.mp4 (9.1 MB)
• 6. Why Now.vtt (3.0 KB)
• 7. Building Block Introduction.mp4 (14.2 MB)
• 7. Building Block Introduction.vtt (5.1 KB)
• 8. Tensors.mp4 (16.9 MB)
• 8. Tensors.vtt (4.3 KB)
• 9. Tensor Operations.mp4 (88.8 MB)
• 9. Tensor Operations.vtt (18.9 KB)

• 1. Outline.mp4 (63.7 MB)
• 1. Outline.vtt (4.1 KB)
• 10. Training Your CNN 1.mp4 (124.9 MB)
• 10. Training Your CNN 1.vtt (15.2 KB)
• 11. Training Your CNN 2.mp4 (128.5 MB)
• 11. Training Your CNN 2.vtt (22.4 KB)
• 12. Loading Previously Trained Model.mp4 (11.2 MB)
• 12. Loading Previously Trained Model.vtt (1.6 KB)
• 13. Model Performance Comparison.mp4 (79.8 MB)
• 13. Model Performance Comparison.vtt (10.7 KB)
• 14. Data Augmentation.mp4 (28.5 MB)
• 14. Data Augmentation.vtt (3.3 KB)
• 15. Transfer Learning.mp4 (97.0 MB)
• 15. Transfer Learning.vtt (12.1 KB)
• 16. Feature Extraction.mp4 (111.1 MB)
• 16. Feature Extraction.vtt (12.9 KB)
• 17. State of the Art Tools.mp4 (35.4 MB)
• 17. State of the Art Tools.vtt (6.0 KB)
• 2. Neural Network Revision.mp4 (43.8 MB)
• 2. Neural Network Revision.vtt (9.2 KB)
• 3. Motivational Example.mp4 (66.2 MB)
• 3. Motivational Example.vtt (8.7 KB)
• 4. Visualizing CNN.mp4 (141.9 MB)
• 4. Visualizing CNN.vtt (15.4 KB)
• 5. Understanding CNN.mp4 (30.0 MB)
• 5. Understanding CNN.vtt (6.7 KB)
• 6. Layer - Input.mp4 (29.1 MB)
• 6. Layer - Input.vtt (6.2 KB)
• 7. Layer - Filter.mp4 (84.4 MB)
• 7. Layer - Filter.vtt (18.5 KB)
• 8. Activation Function.mp4 (32.3 MB)
• 8. Activation Function.vtt (6.9 KB)
• 9. Pooling, Flatten, Dense.mp4 (88.1 MB)
• 9. Pooling, Flatten, Dense.vtt (12.5 KB)

• 1. Installing Applications and Creating Environment.mp4 (38.4 MB)
• 1. Installing Applications and Creating Environment.vtt (6.0 KB)
• 2. Hello World.mp4 (51.2 MB)
• 2. Hello World.vtt (12.5 KB)
• 3. Iris Project 1 Working with Error Messages.mp4 (89.8 MB)
• 3. Iris Project 1 Working with Error Messages.vtt (14.5 KB)
• 4. Iris Project 2 Reading CSV Data into Memory.mp4 (64.6 MB)
• 4. Iris Project 2 Reading CSV Data into Memory.vtt (10.0 KB)
• 5. Iris Project 3 Loading data from Seaborn.mp4 (55.9 MB)
• 5. Iris Project 3 Loading data from Seaborn.vtt (9.9 KB)
• 6. Iris Project 4 Visualization.mp4 (93.5 MB)
• 6. Iris Project 4 Visualization.vtt (11.5 KB)

• 1. Scikit-Learn.mp4 (48.5 MB)
• 1. Scikit-Learn.vtt (10.0 KB)
• 10. Multiple Regression 2.mp4 (91.2 MB)
• 10. Multiple Regression 2.vtt (13.8 KB)
• 11. Regularized Regression.mp4 (44.3 MB)
• 11. Regularized Regression.vtt (7.8 KB)
• 12. Polynomial Regression.mp4 (110.8 MB)
• 12. Polynomial Regression.vtt (19.7 KB)
• 13. Dealing with Non-linear Relationships.mp4 (62.7 MB)
• 13. Dealing with Non-linear Relationships.vtt (10.3 KB)
• 14. Feature Importance.mp4 (36.3 MB)
• 14. Feature Importance.vtt (5.4 KB)
• 15. Data Preprocessing.mp4 (135.5 MB)
• 15. Data Preprocessing.vtt (25.5 KB)
• 16. Variance-Bias Trade Off.mp4 (68.7 MB)
• 16. Variance-Bias Trade Off.vtt (13.7 KB)
• 17. Learning Curve.mp4 (56.4 MB)
• 17. Learning Curve.vtt (10.2 KB)
• 18. Cross Validation.mp4 (48.0 MB)
• 18. Cross Validation.vtt (9.7 KB)
• 19. CV Illustration.mp4 (127.2 MB)
• 19. CV Illustration.vtt (19.9 KB)
• 2. EDA.mp4 (151.7 MB)
• 2. EDA.vtt (22.4 KB)
• 3. Correlation Analysis and Feature Selection.mp4 (22.6 MB)
• 3. Correlation Analysis and Feature Selection.vtt (9.8 KB)
• 3.1 0305.zip.zip (2.1 MB)
• 4. Correlation Analysis and Feature Selection.mp4 (105.2 MB)
• 4. Correlation Analysis and Feature Selection.vtt (13.9 KB)
• 5. Linear Regression with Scikit-Learn.mp4 (77.0 MB)
• 5. Linear Regression with Scikit-Learn.vtt (14.9 KB)
• 6. Five Steps Machine Learning Process.mp4 (77.3 MB)
• 6. Five Steps Machine Learning Process.vtt (9.2 KB)
• 7. Robust Regression.mp4 (119.1 MB)
• 7. Robust Regression.vtt (20.1 KB)
• 8. Evaluate Regression Model Performance.mp4 (99.7 MB)
• 8. Evaluate Regression Model Performance.vtt (17.9 KB)
• 9. Multiple Regression 1.mp4 (125.5 MB)
• 9. Multiple Regression 1.vtt (22.5 KB)

• 1. Logistic Regression.mp4 (119.6 MB)
• 1. Logistic Regression.vtt (23.5 KB)
• 10. Precision Recall Tradeoff.mp4 (102.0 MB)
• 10. Precision Recall Tradeoff.vtt (20.8 KB)
• 11. Altering the Precision Recall Tradeoff.mp4 (20.9 MB)
• 11. Altering the Precision Recall Tradeoff.vtt (3.5 KB)
• 12. ROC.mp4 (52.2 MB)
• 12. ROC.vtt (7.6 KB)
• 2. Introduction to Classification.mp4 (42.1 MB)
• 2. Introduction to Classification.vtt (5.7 KB)
• 3. Understanding MNIST.mp4 (109.0 MB)
• 3. Understanding MNIST.vtt (16.4 KB)
• 4. SGD.mp4 (57.3 MB)
• 4. SGD.vtt (10.6 KB)
• 5. Performance Measure and Stratified k-Fold.mp4 (51.5 MB)
• 5. Performance Measure and Stratified k-Fold.vtt (8.1 KB)
• 6. Confusion Matrix.mp4 (54.7 MB)
• 6. Confusion Matrix.vtt (11.0 KB)
• 7. Precision.mp4 (23.6 MB)
• 7. Precision.vtt (4.1 KB)
• 8. Recall.mp4 (19.6 MB)
• 8. Recall.vtt (3.7 KB)
• 9. f1.mp4 (12.1 MB)
• 9. f1.vtt (2.3 KB)

• 1. Support Vector Machine (SVM) Concepts.mp4 (37.9 MB)
• 1. Support Vector Machine (SVM) Concepts.vtt (8.0 KB)
• 2. Linear SVM Classification.mp4 (80.9 MB)
• 2. Linear SVM Classification.vtt (12.1 KB)
• 3. Polynomial Kernel.mp4 (35.0 MB)
• 3. Polynomial Kernel.vtt (5.5 KB)
• 4. Radial Basis Function.mp4 (70.1 MB)
• 4. Radial Basis Function.vtt (8.8 KB)
• 5. Support Vector Regression.mp4 (59.7 MB)
• 5. Support Vector Regression.vtt (9.3 KB)

• 1. Introduction to Decision Tree.mp4 (43.9 MB)
• 1. Introduction to Decision Tree.vtt (7.9 KB)
• 2. Training and Visualizing a Decision Tree.mp4 (51.4 MB)
• 2. Training and Visualizing a Decision Tree.vtt (7.0 KB)
• 3. Visualizing Boundary.mp4 (54.7 MB)
• 3. Visualizing Boundary.vtt (8.8 KB)
• 4. Tree Regression, Regularization and Over Fitting.mp4 (40.1 MB)
• 4. Tree Regression, Regularization and Over Fitting.vtt (5.3 KB)
• 5. End to End Modeling.mp4 (35.6 MB)
• 5. End to End Modeling.vtt (5.3 KB)
• 6. Project HR.mp4 (177.8 MB)
• 6. Project HR.vtt (28.1 KB)
• 7. Project HR with Google Colab.mp4 (66.6 MB)
• 7. Project HR with Google Colab.vtt (11.4 KB)

• 1. Ensemble Learning Methods Introduction.mp4 (37.2 MB)
• 1. Ensemble Learning Methods Introduction.vtt (5.6 KB)
• 10. Ensemble of ensembles Part 2.mp4 (37.8 MB)
• 10. Ensemble of ensembles Part 2.vtt (5.7 KB)
• 2. Bagging.mp4 (165.4 MB)
• 2. Bagging.vtt (21.1 KB)
• 3. Random Forests and Extra-Trees.mp4 (80.3 MB)
• 3. Random Forests and Extra-Trees.vtt (11.1 KB)
• 4. AdaBoost.mp4 (49.8 MB)
• 4. AdaBoost.vtt (7.9 KB)
• 5. Gradient Boosting Machine.mp4 (22.0 MB)
• 5. Gradient Boosting Machine.vtt (3.6 KB)
• 6. XGBoost Installation.mp4 (22.3 MB)
• 6. XGBoost Installation.vtt (2.8 KB)
• 7. XGBoost.mp4 (35.1 MB)
• 7. XGBoost.vtt (5.1 KB)
• 8. Project HR - Human Resources Analytics.mp4 (59.2 MB)
• 8. Project HR - Human Resources Analytics.vtt (9.5 KB)
• 9. Ensemble of Ensembles Part 1.mp4 (46.4 MB)
• 9. Ensemble of Ensembles Part 1.vtt (7.3 KB)

• 1. kNN Introduction.mp4 (62.9 MB)
• 1. kNN Introduction.vtt (11.0 KB)
• 2. Project Cancer Detection.mp4 (75.7 MB)
• 2. Project Cancer Detection.vtt (10.0 KB)
• 3. Addition Materials.html (0.3 KB)
• 4. Project Cancer Detection Part 1.mp4 (49.4 MB)
• 4. Project Cancer Detection Part 1.vtt (22.1 KB)
• 4.1 0805.zip.zip (40.8 KB)

• 1. Dimensionality Reduction Concept.mp4 (31.4 MB)
• 1. Dimensionality Reduction Concept.vtt (5.3 KB)
• 2. PCA Introduction.mp4 (49.0 MB)
• 2. PCA Introduction.vtt (8.2 KB)
• 3. Project Wine.mp4 (47.9 MB)
• 3. Project Wine.vtt (7.0 KB)
• 4. Kernel PCA.mp4 (36.6 MB)
• 4. Kernel PCA.vtt (6.1 KB)
• 5. Kernel PCA Demo.mp4 (21.4 MB)
• 5. Kernel PCA Demo.vtt (3.6 KB)
• 6. LDA vs PCA.mp4 (34.1 MB)
• 6. LDA vs PCA.vtt (5.9 KB)
• 7. Project Abalone.mp4 (30.7 MB)
• 7. Project Abalone.vtt (4.3 KB)

• [CourseClub.Me].url (0.0 KB)
• [DesireCourse.Net].url (0.0 KB)