AI / Machine Learning Foundations

What you'll achieve

• Understand the mechanics of core machine learning algorithms
• Train, tune, and evaluate classification and regression models
• Apply clustering algorithms to discover natural groupings in data
• Save, load, and integrate trained models into Python applications

Curriculum

1. 1AI vs. Machine Learning vs. Deep Learning: Demystifying the terms
2. 2Machine learning workflow: Data -> Features -> Model -> Evaluation -> Deployment
3. 3Regression algorithms: Linear, Ridge & decision trees for regression
4. 4Classification algorithms: Logistic Regression, Decision Trees & Random Forests
5. 5Model evaluation, confusion matrix, precision/recall trade-offs
6. 6Hyperparameter tuning using GridSearchCV
7. 7Unsupervised clustering with K-Means and PCA dimensionality reduction orientation
8. 8Model serialization (joblib/pickle) & overview of consuming modern AI APIs
9. 9Project: Complete predictive machine learning pipeline with evaluation report