data-science-complete-tutorial

Why we included this project

Here you get a machine learning course in Jupyter notebook form, starting with NumPy and pandas and moving through preprocessing, linear models, decision trees, SVMs, clustering, and model evaluation. It is written for people who learn scikit-learn faster by running code than by reading documentation: every lesson pairs working examples with the concept behind them. The notebooks follow a teaching order, so a newcomer can work through them start to finish, while someone with more experience can jump to a specific later topic such as feature selection or handling imbalanced classes. The material is tied to edyoda's instructor-led training, so it reads best as a companion to a course rather than a standalone reference. If building solid scikit-learn fundamentals is the goal, this is a practical, example-driven place to start.