Explore the fundamentals of machine learning with scikit-learn through our comprehensive MOOC. Each module covers essential concepts and practical applications, equipping learners with valuable skills in predictive modeling.
| Module # | Description | Topics |
|---|---|---|
| Introduction | Machine Learning Concepts | Overview of key machine learning concepts. |
| Module 1 | Predictive Modeling Pipeline | Data Exploration, Handling Numerical and Categorical Data, Building a Data Pipeline. |
| Module 2 | Selecting the Best Model | Validation and Learning Curves, Bias vs. Variance Trade-off, Model Selection. |
| Module 3 | Hyperparameter Tuning | Manual and Automated Tuning, Grid Search, Randomized Search, Nested Cross-Validation. |
| Module 4 | Linear Models | Non-Linear Feature Engineering, Regularization, Ridge Model. |
| Module 5 | Decision Tree (DT) Models | DT in Classification and Regression, DT Hyperparameters. |
| Module 6 | Ensemble of Models | Bootstrapping, Boosting, Hyperparameter Tuning with Ensemble Methods. |
| Module 7 | Evaluating Model Performance | Baseline Models, Cross-Validation, Classification and Regression Metrics. |
- Videos: Watch the instructional videos provided in the YouTube playlist.
- Exercises: Practice your skills with hands-on exercises marked by _ex in the module directories.
- Quizzes: Engage with 2 to 3 small quizzes throughout each module.
- Wrap-up Quiz: Complete a comprehensive wrap-up quiz at the end of each module, available in the "quizzes" folder and the last IPython notebook file of each module directory.
The files follow the [Module][Part][Order] format for easy navigation through the course content.
Explore the INRIA course materials, code examples, and quizzes in our GitHub repository: scikit-learn-mooc