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

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

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种子哈希：4105824974f98a3c8a55ef524a209615ba9b11bf
文件大小： 6.79G
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收录时间：2021-04-25
最近下载：2025-09-20

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文件列表

  • 6. Tree/6. Project HR.mp4 186.5 MB
  • 7. Ensemble Machine Learning/2. Bagging.mp4 173.5 MB
  • 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 163.2 MB
  • 3. Regression/2. EDA.mp4 159.0 MB
  • 11. Deep Learning/3. Motivational Example - Project MNIST.mp4 152.0 MB
  • 11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 150.8 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 148.8 MB
  • 3. Regression/15. Data Preprocessing.mp4 142.1 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 134.8 MB
  • 3. Regression/19. CV Illustration.mp4 133.4 MB
  • 10. Unsupervised Learning Clustering/1. Clustering.mp4 131.8 MB
  • 3. Regression/9. Multiple Regression 1.mp4 131.6 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 130.9 MB
  • 4. Classification/1. Logistic Regression.mp4 125.4 MB
  • 3. Regression/7. Robust Regression.mp4 124.8 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 116.5 MB
  • 3. Regression/12. Polynomial Regression.mp4 116.2 MB
  • 4. Classification/3. Understanding MNIST.mp4 114.3 MB
  • 3. Regression/4. Correlation Analysis and Feature Selection.mp4 110.3 MB
  • 4. Classification/10. Precision Recall Tradeoff.mp4 107.0 MB
  • 3. Regression/8. Evaluate Regression Model Performance.mp4 104.5 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 101.7 MB
  • 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 98.0 MB
  • 3. Regression/10. Multiple Regression 2.mp4 95.6 MB
  • 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 94.2 MB
  • 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 93.1 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 92.4 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 88.5 MB
  • 5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 84.9 MB
  • 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 84.2 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 83.6 MB
  • 3. Regression/6. Five Steps Machine Learning Process.mp4 81.0 MB
  • 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 81.0 MB
  • 3. Regression/5. Linear Regression with Scikit-Learn.mp4 80.7 MB
  • 11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 79.7 MB
  • 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 79.4 MB
  • 11. Deep Learning/4. Binary Classification Problem.mp4 75.6 MB
  • 5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 73.5 MB
  • 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 73.5 MB
  • 3. Regression/16. Variance-Bias Trade Off.mp4 72.0 MB
  • 6. Tree/7. Project HR with Google Colab.mp4 69.8 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 69.4 MB
  • 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 67.7 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 66.8 MB
  • 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 66.0 MB
  • 3. Regression/13. Dealing with Non-linear Relationships.mp4 65.7 MB
  • 5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 62.6 MB
  • 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 62.1 MB
  • 10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 60.5 MB
  • 4. Classification/4. SGD.mp4 60.1 MB
  • 3. Regression/17. Learning Curve.mp4 59.1 MB
  • 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 58.6 MB
  • 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 57.6 MB
  • 6. Tree/3. Visualizing Boundary.mp4 57.4 MB
  • 4. Classification/6. Confusion Matrix.mp4 57.4 MB
  • 1. Introduction/1. What Does the Course Cover.mp4 57.0 MB
  • 4. Classification/12. ROC.mp4 54.8 MB
  • 4. Classification/5. Performance Measure and Stratified k-Fold.mp4 54.0 MB
  • 6. Tree/2. Training and Visualizing a Decision Tree.mp4 53.9 MB
  • 2. Getting Started with Anaconda/2. Hello World.mp4 53.7 MB
  • 7. Ensemble Machine Learning/4. AdaBoost.mp4 52.3 MB
  • 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 51.8 MB
  • 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 51.4 MB
  • 3. Regression/1. Scikit-Learn.mp4 50.8 MB
  • 3. Regression/18. Cross Validation.mp4 50.4 MB
  • 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 50.2 MB
  • 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 48.7 MB
  • 3. Regression/11. Regularized Regression.mp4 46.5 MB
  • 6. Tree/1. Introduction to Decision Tree.mp4 46.0 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 45.9 MB
  • 4. Classification/2. Introduction to Classification.mp4 44.2 MB
  • 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 42.6 MB
  • 6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 42.0 MB
  • 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 40.3 MB
  • 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 39.7 MB
  • 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 39.7 MB
  • 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 39.3 MB
  • 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 39.0 MB
  • 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 38.4 MB
  • 3. Regression/14. Feature Importance.mp4 38.0 MB
  • 6. Tree/5. End to End Modeling.mp4 37.3 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 37.1 MB
  • 7. Ensemble Machine Learning/7. XGBoost.mp4 36.8 MB
  • 5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 36.7 MB
  • 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 35.8 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 33.9 MB
  • 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 32.9 MB
  • 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 32.2 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 31.5 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 30.5 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 29.9 MB
  • 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 28.8 MB
  • 4. Classification/7. Precision.mp4 24.7 MB
  • 3. Regression/3. Correlation Analysis and Feature Selection.mp4 23.7 MB
  • 11. Deep Learning/2. Neural Network Architecture.mp4 23.5 MB
  • 7. Ensemble Machine Learning/6. XGBoost Installation.mp4 23.3 MB
  • 7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 23.0 MB
  • 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 22.5 MB
  • 4. Classification/11. Altering the Precision Recall Tradeoff.mp4 21.9 MB
  • 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 21.9 MB
  • 4. Classification/8. Recall.mp4 20.6 MB
  • 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 19.6 MB
  • 12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 17.7 MB
  • 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14.8 MB
  • 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 14.4 MB
  • 4. Classification/9. f1.mp4 12.7 MB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11.8 MB
  • 12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9.5 MB
  • 3. Regression/3.1 0305.zip.zip 2.2 MB
  • 8. k-Nearest Neighbours (kNN)/4.1 0805.zip.zip 41.7 kB
  • 6. Tree/6. Project HR.vtt 28.8 kB
  • 3. Regression/15. Data Preprocessing.vtt 26.1 kB
  • 11. Deep Learning/1. Estimating Simple Function with Neural Networks.vtt 24.9 kB
  • 11. Deep Learning/3. Motivational Example - Project MNIST.vtt 24.1 kB
  • 4. Classification/1. Logistic Regression.vtt 24.0 kB
  • 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.vtt 23.6 kB
  • 3. Regression/9. Multiple Regression 1.vtt 23.0 kB
  • 3. Regression/2. EDA.vtt 23.0 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.vtt 22.9 kB
  • 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.vtt 22.6 kB
  • 7. Ensemble Machine Learning/2. Bagging.vtt 21.6 kB
  • 4. Classification/10. Precision Recall Tradeoff.vtt 21.3 kB
  • 3. Regression/7. Robust Regression.vtt 20.6 kB
  • 3. Regression/19. CV Illustration.vtt 20.3 kB
  • 3. Regression/12. Polynomial Regression.vtt 20.2 kB
  • 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.vtt 19.3 kB
  • 10. Unsupervised Learning Clustering/1. Clustering.vtt 19.2 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.vtt 18.9 kB
  • 3. Regression/8. Evaluate Regression Model Performance.vtt 18.3 kB
  • 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.vtt 17.1 kB
  • 4. Classification/3. Understanding MNIST.vtt 16.8 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.vtt 15.7 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.vtt 15.6 kB
  • 3. Regression/5. Linear Regression with Scikit-Learn.vtt 15.3 kB
  • 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.vtt 14.8 kB
  • 3. Regression/4. Correlation Analysis and Feature Selection.vtt 14.3 kB
  • 3. Regression/10. Multiple Regression 2.vtt 14.1 kB
  • 3. Regression/16. Variance-Bias Trade Off.vtt 14.0 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.vtt 13.2 kB
  • 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.vtt 12.9 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.vtt 12.8 kB
  • 2. Getting Started with Anaconda/2. Hello World.vtt 12.8 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.vtt 12.4 kB
  • 5. Support Vector Machine (SVM)/2. Linear SVM Classification.vtt 12.4 kB
  • 11. Deep Learning/5. Natural Language Processing - Binary Classification.vtt 12.0 kB
  • 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.vtt 11.8 kB
  • 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.vtt 11.8 kB
  • 11. Deep Learning/4. Binary Classification Problem.vtt 11.7 kB
  • 6. Tree/7. Project HR with Google Colab.vtt 11.7 kB
  • 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.vtt 11.7 kB
  • 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.vtt 11.5 kB
  • 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.vtt 11.3 kB
  • 4. Classification/6. Confusion Matrix.vtt 11.3 kB
  • 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.vtt 11.3 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.vtt 10.9 kB
  • 4. Classification/4. SGD.vtt 10.8 kB
  • 3. Regression/13. Dealing with Non-linear Relationships.vtt 10.5 kB
  • 3. Regression/17. Learning Curve.vtt 10.5 kB
  • 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.vtt 10.3 kB
  • 10. Unsupervised Learning Clustering/2. k_Means Clustering.vtt 10.2 kB
  • 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.vtt 10.2 kB
  • 3. Regression/1. Scikit-Learn.vtt 10.2 kB
  • 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.vtt 10.2 kB
  • 3. Regression/3. Correlation Analysis and Feature Selection.vtt 10.0 kB
  • 3. Regression/18. Cross Validation.vtt 9.9 kB
  • 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.vtt 9.7 kB
  • 5. Support Vector Machine (SVM)/5. Support Vector Regression.vtt 9.5 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.vtt 9.4 kB
  • 3. Regression/6. Five Steps Machine Learning Process.vtt 9.4 kB
  • 6. Tree/3. Visualizing Boundary.vtt 9.0 kB
  • 5. Support Vector Machine (SVM)/4. Radial Basis Function.vtt 9.0 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.vtt 8.9 kB
  • 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.vtt 8.4 kB
  • 4. Classification/5. Performance Measure and Stratified k-Fold.vtt 8.3 kB
  • 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.vtt 8.2 kB
  • 6. Tree/1. Introduction to Decision Tree.vtt 8.1 kB
  • 7. Ensemble Machine Learning/4. AdaBoost.vtt 8.1 kB
  • 3. Regression/11. Regularized Regression.vtt 8.0 kB
  • 4. Classification/12. ROC.vtt 7.8 kB
  • 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.vtt 7.5 kB
  • 11. Deep Learning/2. Neural Network Architecture.vtt 7.4 kB
  • 6. Tree/2. Training and Visualizing a Decision Tree.vtt 7.2 kB
  • 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.vtt 7.2 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.vtt 7.0 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.vtt 6.9 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.vtt 6.4 kB
  • 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.vtt 6.2 kB
  • 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.vtt 6.1 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.vtt 6.1 kB
  • 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.vtt 6.0 kB
  • 4. Classification/2. Introduction to Classification.vtt 5.9 kB
  • 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.vtt 5.8 kB
  • 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.vtt 5.7 kB
  • 5. Support Vector Machine (SVM)/3. Polynomial Kernel.vtt 5.6 kB
  • 3. Regression/14. Feature Importance.vtt 5.5 kB
  • 6. Tree/5. End to End Modeling.vtt 5.5 kB
  • 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.vtt 5.4 kB
  • 6. Tree/4. Tree Regression, Regularization and Over Fitting.vtt 5.4 kB
  • 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.vtt 5.4 kB
  • 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.vtt 5.2 kB
  • 7. Ensemble Machine Learning/7. XGBoost.vtt 5.2 kB
  • 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.vtt 5.2 kB
  • 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.vtt 5.0 kB
  • 12. Appendix A1 Foundations of Deep Learning/8. Tensors.vtt 4.4 kB
  • 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.vtt 4.4 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.vtt 4.2 kB
  • 4. Classification/7. Precision.vtt 4.2 kB
  • 4. Classification/8. Recall.vtt 3.7 kB
  • 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.vtt 3.7 kB
  • 7. Ensemble Machine Learning/5. Gradient Boosting Machine.vtt 3.7 kB
  • 4. Classification/11. Altering the Precision Recall Tradeoff.vtt 3.6 kB
  • 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.vtt 3.4 kB
  • 12. Appendix A1 Foundations of Deep Learning/6. Why Now.vtt 3.1 kB
  • 1. Introduction/1. What Does the Course Cover.vtt 3.0 kB
  • 7. Ensemble Machine Learning/6. XGBoost Installation.vtt 2.9 kB
  • 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.vtt 2.6 kB
  • 4. Classification/9. f1.vtt 2.3 kB
  • 1. Introduction/2. How to Succeed in This Course.html 2.3 kB
  • 1. Introduction/3. Project Files and Resources.html 1.8 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
  • [DesireCourse.Net].url 51 Bytes
  • [CourseClub.Me].url 48 Bytes

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