This course provides a complete introduction to Supervised and Unsupervised Machine Learning, two core branches of artificial intelligence that power modern data-driven systems. You will learn how machines analyze data, identify patterns, and make predictions using structured and unstructured datasets.

In the supervised learning section, you will explore algorithms used for prediction and classification tasks, including Linear Regression, Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). You will understand how models are trained using labeled data and how to evaluate their performance using common metrics.

In the unsupervised learning section, you will discover how to work with unlabeled data using techniques such as K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA). You will learn how to uncover hidden patterns, group similar data points, and reduce dimensionality for better insights.

By the end of this course, you will be able to choose the right machine learning approach for different business problems and confidently apply core algorithms in practical scenarios. This course is ideal for beginners, data analysts, and aspiring data scientists.