This is a collection of notebooks to learn Machine Learning. I am using the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow from Aurelien Géron.
Each notebook is a chapter from the book (except for the first one). The notebooks are organized as follows:
- Chapter 2: End-to-End Machine Learning Project
- Chapter 3: Classification
- Chapter 4: Training Linear Models
- Chapter 5: Support Vector Machines
- Chapter 6: Decision Trees
- Chapter 7: Ensemble Learning and Random Forests
- Chapter 8: Dimensionality Reduction
- Chapter 9: Unsupervised Learning Techniques
My solutions to (some of) the exercises are in the exercises folder.
Also, I am building some functions (that are already well implemented in Scikit-learn) from scratch to understand them better. They are in the MyClasses folder.
Currently:
- Process Data: A class to process data (1 hot encoder, normalization, split data in train, test, val sets, ...).
- Metrics: A class to calculate metrics (accuracy, precision, recall, rmse, ...).
- Aurelien Géron for writing the book. I am learning a lot from it.