Machine Learning Graduate Prep: Math, Programming & Data Science

Preparing for a graduate program or formal entrance assessment in machine learning requires a firm grasp of essential baseline skills. This written course guides you through the foundational topics in mathematics, programming, and data science needed to succeed in academic and technical evaluations. You will build confidence by reading clear explanations of core theories and working through structured practice problems. What you'll learn: - Understand key mathematical concepts in linear algebra, calculus, probability, and statistics. - Practice core Python programming principles, control flow, and data structure manipulation. - Analyze data using foundational techniques and modern libraries like NumPy and pandas. - Master essential machine learning paradigms, model evaluation techniques, and baseline algorithms. - Explore contemporary topics such as vector embeddings and foundational neural network concepts. - Work through practice questions designed to reflect real-world entrance assessment formats. The course begins with clear definitions of key terminology and mathematical foundations before progressing to practical logic and data science applications. It is ideal for prospective graduate students and self-taught developers seeking to validate their foundation. Read through the material and take the next step toward your advanced machine learning education.