Machine Learning Basics
Let's get started with Machine Learning from the ground up using Python + scikit-learn — a 25-lesson tutorial that walks you step by step from Data Preprocessing, Regression, and Classification through Random Forest, a Neural Network intro, Hyperparameter Tuning, all the way to a production-ready project.
25 Lessons · 220 min · Learning Platform Editorial Team
What you need on the desk
- Python 3.9+ (install from python.org)
- scikit-learn, Pandas, NumPy, Matplotlib (via pip install)
- Jupyter Notebook (interactive development environment)
Know this much before you start
- You should understand the basics of Python syntax (it helps to have gone through the site's Python tutorial first)
- You should be comfortable with NumPy/Pandas basics (the site's NumPy/Pandas tutorial is recommended)
- Optional: a basic grounding in statistics/algebra will help even more
By the end, you can
- Explain Machine Learning in terms of Supervised, Unsupervised, and Reinforcement Learning
- Properly carry out Data Preprocessing, Feature Engineering, and Train/Test Split
- Write Regression (Linear Regression) and Classification (Logistic Regression, Decision Tree, KNN) algorithms
- Understand and apply Random Forest, SVM, Clustering, PCA, and a Neural Network intro
- Correctly carry out Model Evaluation Metrics (Precision/Recall/F1), Cross-Validation, and Hyperparameter Tuning
- Build a House Price Prediction and a Customer Churn Classification project to completion, following a production readiness checklist