This textbook is about the Machine Learning for Data Analysis (ML) course which will instruct students in the fundamentals of machine learning methods through examples in the biological phenomenon and clinical data analysis. This course is designed to share knowledge of real-world data science and aid to learn complex machine learning theory, algorithms, and coding libraries in a simple way. The structure of this course is designed to walk students step-by-step into the world of machine learning. The course will cover major topics in Machine Learning such as supervised learning (i.e., regression, classification), unsupervised learning, association rule learning, reinforcement learning, deep learning, dimensionality reduction, and model selection and boosting. This course is packed with practical machine-learning exercises that are based on real-life examples. Students will learn machine learning theory, but they will also get hands-on practice building their models using programming tools such as Python.