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| [FreeCoursesOnline.Me].url |
133б |
| [FreeTutorials.Us].url |
119б |
| [FTU Forum].url |
252б |
| 001. Introduction.mp4 |
31.05Мб |
| 001. Introduction.srt |
16.07Кб |
| 002. Key Concepts in Machine Learning.mp4 |
44.56Мб |
| 002. Key Concepts in Machine Learning.srt |
18.82Кб |
| 003. Python Tools for Machine Learning.mp4 |
12.86Мб |
| 003. Python Tools for Machine Learning.srt |
6.11Кб |
| 004. An Example Machine Learning Problem.mp4 |
31.73Мб |
| 004. An Example Machine Learning Problem.srt |
14.83Кб |
| 005. Examining the Data.mp4 |
32.24Мб |
| 005. Examining the Data.srt |
12.05Кб |
| 006. K-Nearest Neighbors Classification.mp4 |
36.25Мб |
| 006. K-Nearest Neighbors Classification.srt |
26.19Кб |
| 007. Introduction to Supervised Machine Learning.mp4 |
37.88Мб |
| 007. Introduction to Supervised Machine Learning.srt |
22.13Кб |
| 008. Overfitting and Underfitting.mp4 |
19.51Мб |
| 008. Overfitting and Underfitting.srt |
15.81Кб |
| 009. Supervised Learning Datasets.mp4 |
11.22Мб |
| 009. Supervised Learning Datasets.srt |
6.74Кб |
| 010. K-Nearest Neighbors Classification and Regression.mp4 |
22.53Мб |
| 010. K-Nearest Neighbors Classification and Regression.srt |
17.09Кб |
| 011. Linear Regression Least-Squares.mp4 |
30.08Мб |
| 011. Linear Regression Least-Squares.srt |
21.26Кб |
| 012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 |
39.93Мб |
| 012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt |
27.19Кб |
| 013. Logistic Regression.mp4 |
20.30Мб |
| 013. Logistic Regression.srt |
17.13Кб |
| 014. Linear Classifiers Support Vector Machines.mp4 |
22.69Мб |
| 014. Linear Classifiers Support Vector Machines.srt |
15.54Кб |
| 015. Multi-Class Classification.mp4 |
15.41Мб |
| 015. Multi-Class Classification.srt |
8.30Кб |
| 016. Kernelized Support Vector Machines.mp4 |
39.14Мб |
| 016. Kernelized Support Vector Machines.srt |
25.60Кб |
| 017. Cross-Validation.mp4 |
20.00Мб |
| 017. Cross-Validation.srt |
13.00Кб |
| 018. Decision Trees.mp4 |
37.83Мб |
| 018. Decision Trees.srt |
28.36Кб |
| 019. Model Evaluation & Selection.mp4 |
46.10Мб |
| 019. Model Evaluation & Selection.srt |
30.08Кб |
| 020. Confusion Matrices & Basic Evaluation Metrics.mp4 |
20.75Мб |
| 020. Confusion Matrices & Basic Evaluation Metrics.srt |
15.85Кб |
| 021. Classifier Decision Functions.mp4 |
12.65Мб |
| 021. Classifier Decision Functions.srt |
9.04Кб |
| 022. Precision-recall and ROC curves.mp4 |
9.23Мб |
| 022. Precision-recall and ROC curves.srt |
7.53Кб |
| 023. Multi-Class Evaluation.mp4 |
19.77Мб |
| 023. Multi-Class Evaluation.srt |
15.21Кб |
| 024. Regression Evaluation.mp4 |
17.01Мб |
| 024. Regression Evaluation.srt |
7.83Кб |
| 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 |
34.50Мб |
| 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt |
18.12Кб |
| 026. Naive Bayes Classifiers.mp4 |
21.38Мб |
| 026. Naive Bayes Classifiers.srt |
11.20Кб |
| 027. Random Forests.mp4 |
26.45Мб |
| 027. Random Forests.srt |
17.07Кб |
| 028. Gradient Boosted Decision Trees.mp4 |
11.81Мб |
| 028. Gradient Boosted Decision Trees.srt |
8.44Кб |
| 029. Neural Networks.mp4 |
41.51Мб |
| 029. Neural Networks.srt |
27.90Кб |
| 030. Deep Learning (Optional).mp4 |
17.46Мб |
| 030. Deep Learning (Optional).srt |
10.34Кб |
| 031. Data Leakage.mp4 |
32.89Мб |
| 031. Data Leakage.srt |
16.69Кб |
| 032. Introduction.mp4 |
10.67Мб |
| 032. Introduction.srt |
6.46Кб |
| 033. Dimensionality Reduction and Manifold Learning.mp4 |
16.09Мб |
| 033. Dimensionality Reduction and Manifold Learning.srt |
13.47Кб |
| 034. Clustering.mp4 |
27.18Мб |
| 034. Clustering.srt |
19.90Кб |
| 035. Conclusion.mp4 |
9.89Мб |
| 035. Conclusion.srt |
3.90Кб |