Course materials for a 3-day bespoke Machine Learning course based (partially) on the book "Introduction to Statistical Learning with Python".

Morning session

• Introduction to Statistical Learning (Chapter 2).
• Lab: Data exploration and visualization with Python.
• Regression (Chapter 3).

Afternoon session

• Classification (Chapter 4).
• Lab: Scikit-learn. Regression and classification. Model selection.

Morning session

• Tree methods and random forests (Chapter 8).
• Support Vector Machines (Chapter 9).
• Lab: Scikit-learn. Hyperparameter tuning.

Afternoon session

• Neural networks and Deep Learning (Chapter 10).
• Lab: Deep learning with keras.
• (Optional Lab): Introduction to pytorch.

Morning session

• Unsupervised Learning.
• Lab: k-means.
• Reinforcement Learning.
• Lab: OpenAI Gym.

Afternoon session

• Introduction to natural language processing.
• Transformer networks and attention mechanism.
• Consuming models from APIs.

• "Introduction to Statistical Learning with Python".
• Slides from the book
• Repository from the book
• HuggingFace Example Notebooks