This course provides a comprehensive overview of the three major types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. It is designed to help learners clearly understand how each approach works, when to use it, and how it applies to real-world data science problems.

You will begin with supervised learning, where models are trained using labeled data to perform tasks such as regression and classification. Key algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines will be introduced.

Next, the course explores unsupervised learning, which focuses on identifying hidden patterns in unlabeled data. You will learn clustering techniques like K-Means and Hierarchical Clustering, along with dimensionality reduction methods such as Principal Component Analysis (PCA).

Finally, the course introduces reinforcement learning, where agents learn by interacting with an environment and receiving rewards or penalties. Concepts such as reward systems, policies, and real-world applications in robotics and gaming will be covered.

By the end of this course, you will clearly understand the differences between these learning approaches and be able to select the right technique for different data science scenarios.