Machine Learning Fundamentals
4h 5m 9s
English
Paid
Course description
Advance your career in machine learning with confidence. Master the key ML fundamentals that are in demand by employers and acquire the skills necessary to solve real-world problems. Start building a successful future in the tech field today!
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| # | Title | Duration |
|---|---|---|
| 1 | Introduction | 06:55 |
| 2 | 1. Machine Learning Basics | 15:27 |
| 3 | 2. Bias-Variance Trade-off | 07:11 |
| 4 | 3. Overfitting Regularization | 15:31 |
| 5 | 4.1 Linear Regression - Causal Analysis (Part 1) | 14:51 |
| 6 | 4.2 Linear Regression - (Part 2) | 24:25 |
| 7 | 5. Logistic Regression & Maximum Likelihood Estimation (MLE) | 15:09 |
| 8 | 6. Linear Discriminant Analysis (LDA) | 11:15 |
| 9 | 7. K-Nearest Neighbors (KNN) | 11:52 |
| 10 | 8. Decision Trees | 16:45 |
| 11 | 9. Bagging | 07:54 |
| 12 | 10. Random Forest | 08:11 |
| 13 | 11. (Boosting Part 1) Introduction | 08:01 |
| 14 | 12. Boosting (Part 2) - AdaBoost | 10:00 |
| 15 | 13. Boosting (Part 3) - Gradient Boosting Model (GBM) | 10:53 |
| 16 | 14. Boosting (Part 4) - XGBoost | 08:08 |
| 17 | 15. Clustering (Part 1) - K-Means & Elbow Method | 17:43 |
| 18 | 16. Clustering (Part 2) - Hierarchical Clustering | 10:49 |
| 19 | 17. Clustering (Part 3) - DBScan | 07:45 |
| 20 | 18. Dimensionality Reduction (Part 1) - Feature Selection | 07:32 |
| 21 | 19. Dimensionality Reduction (Part 2) - Principal Component Analysis | 08:52 |
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