This comprehensive course will guide you through the core techniques of supervised machine learning using Python. You will begin with an introduction to essential concepts and then dive into hands-on coding exercises, where you’ll implement key algorithms such as K-Nearest Neighbor (KNN), Naive Bayes, and Decision Trees. Working with real datasets like MNIST, you'll gain practical experience in solving problems and understanding how these algorithms perform in the real world. As you progress, you'll develop practical skills in hyperparameter tuning, cross-validation, and feature extraction, which are critical for optimizing machine learning models. You'll explore advanced topics like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and non-Naive Bayes methods, adding depth to your knowledge of more complex machine learning techniques. The course culminates in learning how to deploy machine learning models as web services, providing you with a complete understanding of machine learning application in real-world. Targeted at aspiring data scientists and machine learning engineers, this course combines theory and practical exercises to ensure you build strong, deployable machine learning models. While no prior machine learning experience is required, familiarity with Python programming is recommended.