practical-machine-learning-with-python

Why we included this project

This companion repository holds the notebooks, code, and examples that accompany Dipanjan Sarkar's Apress book of the same name. The material is organized as a three-tiered course: foundations of the Python data and ML ecosystem, then the full modeling pipeline from data wrangling and feature engineering through tuning and evaluation, and finally applied case studies that tie the skills together. Because the notebooks are built on scikit-learn, pandas, statsmodels, spaCy, NLTK, TensorFlow, and Keras, working through them doubles as a tour of the mainstream Python stack rather than a single framework. It is a solid learn-by-doing path for self-learners and instructors who want structured practice before taking on their own projects.