Machine Learning Foundations and Projects

About this path

This path guides you through the essentials of machine learning, from basic definitions to popular algorithms and real-world projects. You'll explore supervised, unsupervised, and reinforcement learning, and see how to apply techniques like linear regression, KNN, decision trees, and clustering. The playlist includes practical tutorials using Python, plus project walkthroughs for stock prediction, fake news detection, and more. It's suitable for beginners and those wanting a broad refresher.

What you will learn

• Define machine learning and distinguish it from deep learning and AI.
• Explain supervised, unsupervised, and reinforcement learning with examples.
• Describe common algorithms: linear regression, KNN, decision trees, K-means, PCA, Q-learning.
• Identify overfitting and underfitting and basic feature selection techniques.
• Apply machine learning to practical projects like stock prediction, fake news detection, and disease prediction.

Who this is for

Beginners who want a structured free introduction before deeper practice.

Curriculum