[Coursera] Applied Machine Learning in Python

Code:

• 001. Introduction.mp4 (31.1 MB)
• 001. Introduction.srt (16.1 KB)
• 002. Key Concepts in Machine Learning.mp4 (44.6 MB)
• 002. Key Concepts in Machine Learning.srt (18.8 KB)
• 003. Python Tools for Machine Learning.mp4 (12.9 MB)
• 003. Python Tools for Machine Learning.srt (6.1 KB)
• 004. An Example Machine Learning Problem.mp4 (31.7 MB)
• 004. An Example Machine Learning Problem.srt (14.8 KB)
• 005. Examining the Data.mp4 (32.2 MB)
• 005. Examining the Data.srt (12.1 KB)
• 006. K-Nearest Neighbors Classification.mp4 (36.2 MB)
• 006. K-Nearest Neighbors Classification.srt (26.2 KB)
• 007. Introduction to Supervised Machine Learning.mp4 (37.9 MB)
• 007. Introduction to Supervised Machine Learning.srt (22.1 KB)
• 008. Overfitting and Underfitting.mp4 (19.5 MB)
• 008. Overfitting and Underfitting.srt (15.8 KB)
• 009. Supervised Learning Datasets.mp4 (11.2 MB)
• 009. Supervised Learning Datasets.srt (6.7 KB)
• 010. K-Nearest Neighbors Classification and Regression.mp4 (22.5 MB)
• 010. K-Nearest Neighbors Classification and Regression.srt (17.1 KB)
• 011. Linear Regression Least-Squares.mp4 (30.1 MB)
• 011. Linear Regression Least-Squares.srt (21.3 KB)
• 012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 (39.9 MB)
• 012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt (27.2 KB)
• 013. Logistic Regression.mp4 (20.3 MB)
• 013. Logistic Regression.srt (17.1 KB)
• 014. Linear Classifiers Support Vector Machines.mp4 (22.7 MB)
• 014. Linear Classifiers Support Vector Machines.srt (15.5 KB)
• 015. Multi-Class Classification.mp4 (15.4 MB)
• 015. Multi-Class Classification.srt (8.3 KB)
• 016. Kernelized Support Vector Machines.mp4 (39.1 MB)
• 016. Kernelized Support Vector Machines.srt (25.6 KB)
• 017. Cross-Validation.mp4 (20.0 MB)
• 017. Cross-Validation.srt (13.0 KB)
• 018. Decision Trees.mp4 (37.8 MB)
• 018. Decision Trees.srt (28.4 KB)
• 019. Model Evaluation & Selection.mp4 (46.1 MB)
• 019. Model Evaluation & Selection.srt (30.1 KB)
• 020. Confusion Matrices & Basic Evaluation Metrics.mp4 (20.8 MB)
• 020. Confusion Matrices & Basic Evaluation Metrics.srt (15.8 KB)
• 021. Classifier Decision Functions.mp4 (12.7 MB)
• 021. Classifier Decision Functions.srt (9.0 KB)
• 022. Precision-recall and ROC curves.mp4 (9.2 MB)
• 022. Precision-recall and ROC curves.srt (7.5 KB)
• 023. Multi-Class Evaluation.mp4 (19.8 MB)
• 023. Multi-Class Evaluation.srt (15.2 KB)
• 024. Regression Evaluation.mp4 (17.0 MB)
• 024. Regression Evaluation.srt (7.8 KB)
• 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 (34.5 MB)
• 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt (18.1 KB)
• 026. Naive Bayes Classifiers.mp4 (21.4 MB)
• 026. Naive Bayes Classifiers.srt (11.2 KB)
• 027. Random Forests.mp4 (26.4 MB)
• 027. Random Forests.srt (17.1 KB)
• 028. Gradient Boosted Decision Trees.mp4 (11.8 MB)
• 028. Gradient Boosted Decision Trees.srt (8.4 KB)
• 029. Neural Networks.mp4 (41.5 MB)
• 029. Neural Networks.srt (27.9 KB)
• 030. Deep Learning (Optional).mp4 (17.5 MB)
• 030. Deep Learning (Optional).srt (10.3 KB)
• 031. Data Leakage.mp4 (32.9 MB)
• 031. Data Leakage.srt (16.7 KB)
• 032. Introduction.mp4 (10.7 MB)
• 032. Introduction.srt (6.5 KB)
• 033. Dimensionality Reduction and Manifold Learning.mp4 (16.1 MB)
• 033. Dimensionality Reduction and Manifold Learning.srt (13.5 KB)
• 034. Clustering.mp4 (27.2 MB)
• 034. Clustering.srt (19.9 KB)
• 035. Conclusion.mp4 (9.9 MB)
• 035. Conclusion.srt (3.9 KB)
• [CourseClub.NET].url (0.1 KB)
• [DesireCourse.Com].url (0.0 KB)
• [FreeCourseSite.Com].url (0.1 KB)