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[FreeAllCourse Com] Udemy- The Complete Machine Learning Course with Python
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Torrent Files List
1. Introduction (Size: 6.79 GB) (Files: 221)
1. Introduction
1. What Does the Course Cover.mp4
1. What Does the Course Cover.vtt
2. How to Succeed in This Course.html
3. Project Files and Resources.html
10. Unsupervised Learning Clustering
1. Clustering.mp4
1. Clustering.vtt
2. k_Means Clustering.mp4
2. k_Means Clustering.vtt
11. Deep Learning
1. Estimating Simple Function with Neural Networks.mp4
1. Estimating Simple Function with Neural Networks.vtt
2. Neural Network Architecture.mp4
2. Neural Network Architecture.vtt
3. Motivational Example - Project MNIST.mp4
3. Motivational Example - Project MNIST.vtt
4. Binary Classification Problem.mp4
4. Binary Classification Problem.vtt
5. Natural Language Processing - Binary Classification.mp4
5. Natural Language Processing - Binary Classification.vtt
12. Appendix A1 Foundations of Deep Learning
1. Introduction to Neural Networks.mp4
1. Introduction to Neural Networks.vtt
10. Gradient Based Optimization.mp4
10. Gradient Based Optimization.vtt
11. Getting Started with Neural Network and Deep Learning Libraries.mp4
11. Getting Started with Neural Network and Deep Learning Libraries.vtt
12. Categories of Machine Learning.mp4
12. Categories of Machine Learning.vtt
13. Over and Under Fitting.mp4
13. Over and Under Fitting.vtt
14. Machine Learning Workflow.mp4
14. Machine Learning Workflow.vtt
2. Differences between Classical Programming and Machine Learning.mp4
2. Differences between Classical Programming and Machine Learning.vtt
3. Learning Representations.mp4
3. Learning Representations.vtt
4. What is Deep Learning.mp4
4. What is Deep Learning.vtt
5. Learning Neural Networks.mp4
5. Learning Neural Networks.vtt
6. Why Now.mp4
6. Why Now.vtt
7. Building Block Introduction.mp4
7. Building Block Introduction.vtt
8. Tensors.mp4
8. Tensors.vtt
9. Tensor Operations.mp4
9. Tensor Operations.vtt
13. Computer Vision and Convolutional Neural Network (CNN)
1. Outline.mp4
1. Outline.vtt
10. Training Your CNN 1.mp4
10. Training Your CNN 1.vtt
11. Training Your CNN 2.mp4
11. Training Your CNN 2.vtt
12. Loading Previously Trained Model.mp4
12. Loading Previously Trained Model.vtt
13. Model Performance Comparison.mp4
13. Model Performance Comparison.vtt
14. Data Augmentation.mp4
14. Data Augmentation.vtt
15. Transfer Learning.mp4
15. Transfer Learning.vtt
16. Feature Extraction.mp4
16. Feature Extraction.vtt
17. State of the Art Tools.mp4
17. State of the Art Tools.vtt
2. Neural Network Revision.mp4
2. Neural Network Revision.vtt
3. Motivational Example.mp4
3. Motivational Example.vtt
4. Visualizing CNN.mp4
4. Visualizing CNN.vtt
5. Understanding CNN.mp4
5. Understanding CNN.vtt
6. Layer - Input.mp4
6. Layer - Input.vtt
7. Layer - Filter.mp4
7. Layer - Filter.vtt
8. Activation Function.mp4
8. Activation Function.vtt
9. Pooling, Flatten, Dense.mp4
9. Pooling, Flatten, Dense.vtt
2. Getting Started with Anaconda
1. Installing Applications and Creating Environment.mp4
1. Installing Applications and Creating Environment.vtt
2. Hello World.mp4
2. Hello World.vtt
3. Iris Project 1 Working with Error Messages.mp4
3. Iris Project 1 Working with Error Messages.vtt
4. Iris Project 2 Reading CSV Data into Memory.mp4
4. Iris Project 2 Reading CSV Data into Memory.vtt
5. Iris Project 3 Loading data from Seaborn.mp4
5. Iris Project 3 Loading data from Seaborn.vtt
6. Iris Project 4 Visualization.mp4
6. Iris Project 4 Visualization.vtt
3. Regression
1. Scikit-Learn.mp4
1. Scikit-Learn.vtt
10. Multiple Regression 2.mp4
10. Multiple Regression 2.vtt
11. Regularized Regression.mp4
11. Regularized Regression.vtt
12. Polynomial Regression.mp4
12. Polynomial Regression.vtt
13. Dealing with Non-linear Relationships.mp4
13. Dealing with Non-linear Relationships.vtt
14. Feature Importance.mp4
14. Feature Importance.vtt
15. Data Preprocessing.mp4
15. Data Preprocessing.vtt
16. Variance-Bias Trade Off.mp4
16. Variance-Bias Trade Off.vtt
17. Learning Curve.mp4
17. Learning Curve.vtt
18. Cross Validation.mp4
18. Cross Validation.vtt
19. CV Illustration.mp4
19. CV Illustration.vtt
2. EDA.mp4
2. EDA.vtt
3. Correlation Analysis and Feature Selection.mp4
3. Correlation Analysis and Feature Selection.vtt
3.1 0305.zip.zip
4. Correlation Analysis and Feature Selection.mp4
4. Correlation Analysis and Feature Selection.vtt
5. Linear Regression with Scikit-Learn.mp4
5. Linear Regression with Scikit-Learn.vtt
6. Five Steps Machine Learning Process.mp4
6. Five Steps Machine Learning Process.vtt
7. Robust Regression.mp4
7. Robust Regression.vtt
8. Evaluate Regression Model Performance.mp4
8. Evaluate Regression Model Performance.vtt
9. Multiple Regression 1.mp4
9. Multiple Regression 1.vtt
4. Classification
1. Logistic Regression.mp4
1. Logistic Regression.vtt
10. Precision Recall Tradeoff.mp4
10. Precision Recall Tradeoff.vtt
11. Altering the Precision Recall Tradeoff.mp4
11. Altering the Precision Recall Tradeoff.vtt
12. ROC.mp4
12. ROC.vtt
2. Introduction to Classification.mp4
2. Introduction to Classification.vtt
3. Understanding MNIST.mp4
3. Understanding MNIST.vtt
4. SGD.mp4
4. SGD.vtt
5. Performance Measure and Stratified k-Fold.mp4
5. Performance Measure and Stratified k-Fold.vtt
6. Confusion Matrix.mp4
6. Confusion Matrix.vtt
7. Precision.mp4
7. Precision.vtt
8. Recall.mp4
8. Recall.vtt
9. f1.mp4
9. f1.vtt
5. Support Vector Machine (SVM)
1. Support Vector Machine (SVM) Concepts.mp4
1. Support Vector Machine (SVM) Concepts.vtt
2. Linear SVM Classification.mp4
2. Linear SVM Classification.vtt
3. Polynomial Kernel.mp4
3. Polynomial Kernel.vtt
4. Radial Basis Function.mp4
4. Radial Basis Function.vtt
5. Support Vector Regression.mp4
5. Support Vector Regression.vtt
6. Tree
1. Introduction to Decision Tree.mp4
1. Introduction to Decision Tree.vtt
2. Training and Visualizing a Decision Tree.mp4
2. Training and Visualizing a Decision Tree.vtt
3. Visualizing Boundary.mp4
3. Visualizing Boundary.vtt
4. Tree Regression, Regularization and Over Fitting.mp4
4. Tree Regression, Regularization and Over Fitting.vtt
5. End to End Modeling.mp4
5. End to End Modeling.vtt
6. Project HR.mp4
6. Project HR.vtt
7. Project HR with Google Colab.mp4
7. Project HR with Google Colab.vtt
7. Ensemble Machine Learning
1. Ensemble Learning Methods Introduction.mp4
1. Ensemble Learning Methods Introduction.vtt
10. Ensemble of ensembles Part 2.mp4
10. Ensemble of ensembles Part 2.vtt
2. Bagging.mp4
2. Bagging.vtt
3. Random Forests and Extra-Trees.mp4
3. Random Forests and Extra-Trees.vtt
4. AdaBoost.mp4
4. AdaBoost.vtt
5. Gradient Boosting Machine.mp4
5. Gradient Boosting Machine.vtt
6. XGBoost Installation.mp4
6. XGBoost Installation.vtt
7. XGBoost.mp4
7. XGBoost.vtt
8. Project HR - Human Resources Analytics.mp4
8. Project HR - Human Resources Analytics.vtt
9. Ensemble of Ensembles Part 1.mp4
9. Ensemble of Ensembles Part 1.vtt
8. k-Nearest Neighbours (kNN)
1. kNN Introduction.mp4
1. kNN Introduction.vtt
2. Project Cancer Detection.mp4
2. Project Cancer Detection.vtt
3. Addition Materials.html
4. Project Cancer Detection Part 1.mp4
4. Project Cancer Detection Part 1.vtt
4.1 0805.zip.zip
9. Unsupervised Learning Dimensionality Reduction
1. Dimensionality Reduction Concept.mp4
1. Dimensionality Reduction Concept.vtt
2. PCA Introduction.mp4
2. PCA Introduction.vtt
3. Project Wine.mp4
3. Project Wine.vtt
4. Kernel PCA.mp4
4. Kernel PCA.vtt
5. Kernel PCA Demo.mp4
5. Kernel PCA Demo.vtt
6. LDA vs PCA.mp4
6. LDA vs PCA.vtt
7. Project Abalone.mp4
7. Project Abalone.vtt
Torrent description
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