Machine Learning with Python Tutorial

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Course Contents

ML Foundations

• Machine Learning Introduction
• The End-to-End ML Workflow
• Statistics and Linear Algebra Essentials

Data Preparation

• NumPy Arrays and Vectorization
• Data Analysis with pandas
• Cleaning Data and Handling Missing Values

Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests

Evaluation and Generalization

• Train, Validation, and Test Splits
• Cross-Validation
• Classification and Regression Metrics

Improving Models

• Overfitting and Regularization
• Feature Engineering and Preprocessing
• Hyperparameter Tuning

Unsupervised Learning

• Clustering with K-Means
• Dimensionality Reduction with PCA
• Anomaly Detection

Responsible and Reliable ML

• Data Leakage and Shortcut Learning
• Model Interpretation
• Fairness, Privacy, and Model Risk

ML in Production

• Reproducible Training Pipelines
• Serving Models and Monitoring Drift
• Capstone: Build a Churn Prediction System