Udemy The Complete Machine Learning Course with Python Torrent Download Locations

[DesireCourse.Net] Udemy - The Complete Machine Learning Course with Python
    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 -