Scikit-Learn Machine Learning

Build ML models with Python's most popular library

Author: AI Learning Club Team · Difficulty: Beginner · Duration: 2 hr 15 min · Modules: 26

Modules

1. Introduction to Scikit-Learn (5 min)
2. Setting Up Your Environment (5 min)
3. Understanding Machine Learning Basics (5 min)
4. Linear Regression (5 min)
5. Logistic Regression (5 min)
6. Support Vector Machines (SVM) (5 min)
7. Decision Trees (5 min)
8. Random Forests (5 min)
9. Gradient Boosting Machines (5 min)
10. XGBoost and LightGBM (5 min)
11. Ensemble Methods (5 min)
12. Cross-Validation Techniques (5 min)
13. Hyperparameter Tuning (5 min)
14. Feature Engineering (5 min)
15. Feature Selection (5 min)
16. Model Evaluation Metrics (5 min)
17. Pipelines in Scikit-Learn (5 min)
18. Model Persistence (5 min)
19. Working with Text Data (5 min)
20. Working with Image Data (5 min)
21. Unsupervised Learning: Clustering (5 min)
22. Dimensionality Reduction (5 min)
23. Neural Networks with Scikit-Learn (5 min)
24. Advanced Topics and Best Practices (5 min)
25. Project: End-to-End Machine Learning Pipeline (5 min)
26. Resources & References (2 min)

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