This repository has Notebooks of notes and practice code while following the Machine Learning with Python Cookbook - Practical Solutions from Preprocessing to Deep Learning by Chris Albon. This book is for the machine learning practitioner who, while comfortable with the theory and concepts of machine learning, would benefit from a quick reference containing code to solve challenges.
- Vectors, Matrices, and Arrays
- Loading Data
- Data Wrangling
- Handling Numerical Data
- Handling Categorical Data
- Dimensionality Reduction
- Model Evaluation
- Model Selection
- Linear Regression
- Trees and Forests
- K-Nearest Neighbors
- Logistic Regression
- Support Vector Machines
- Naive Bayes
- Clustering
- Neural Networks
- Saving and Loading Trained Models
