This repository is a curated roadmap for learning the core mathematics used in computer science, machine learning, and data science without getting lost in unnecessary detours. It organizes topics like algebra, calculus, linear algebra, probability, and statistics into a pragmatic sequence that favors intuition and problem-solving over purely formal proofs. The materials emphasize short, high-leverage resources—video lectures, concise notes, and hands-on exercises—that help you build momentum quickly. It also suggests checkpoints and practice ideas so you can test comprehension and move forward with confidence. The structure is useful both for newcomers who want a starting plan and for practitioners filling specific gaps before tackling ML or deep learning. Overall, it acts as a compact study plan that turns “learn math” from a vague goal into a concrete, achievable path.
Features
- Topic-by-topic roadmap for algebra, calculus, linear algebra, probability, and statistics
- Emphasis on intuitive resources and practice-heavy study to build confidence fast
- Lightweight weekly structure to help with pacing and accountability
- Suggestions for problem sets and mini projects to lock in concepts
- Pointers to supplementary material for deeper dives when needed
- Adaptable plan that can be tailored to different backgrounds and timelines