The Machine Learning Specialization is a three‑course sequence that guides you from the foundations of supervised learning through advanced algorithms, and onward to unsupervised, recommender‑system, and reinforcement‑learning techniques. Developed around hands‑on Python practice with libraries such as NumPy, scikit‑learn, and TensorFlow, the specialization balances theory with real‑world application so you can build production‑ready models with confidence.
Course Line‑Up
Supervised Machine Learning: Regression and Classification – 33 hours
Learn to frame prediction problems, engineer features, and train models such as linear regression, logistic regression, CART, and regularized regressors. You’ll gain fluency with scikit‑learn pipelines and evaluation metrics while building data‑driven solutions that avoid overfitting.
Advanced Learning Algorithms – 34 hours
Move beyond the basics to master neural networks, decision‑tree ensembles, random forests, gradient‑boosted trees, and XGBoost. You’ll implement these models in TensorFlow, apply hyper‑parameter tuning, and adopt best practices that ensure your models generalize on unseen data.
Unsupervised Learning, Recommenders, and Reinforcement Learning – 27 hours
Explore clustering, anomaly detection, collaborative‑filtering and deep‑learning recommenders, then dive into deep reinforcement learning. By the end, you can build systems that learn without labels, personalize experiences, and make sequential decisions in dynamic environments.
