AI ML Roadmap from scratch

Rather than overwhelming beginners with every possible resource or tangent, the roadmap prioritizes a specific sequencemathematics foundations, Python proficiency, classical ML, deep learning, then specializationthat mirrors what hiring managers in the industry expect candidates to demonstrate competency across.

The roadmap details what to learn at each stage, why it matters in practice, and which specific resources (books, courses, GitHub projects) have proven most effective for learners following this path.

Mathematics coverage focuses on linear algebra, calculus, probability, and statistics in the applied contexts most relevant to ML rather than as abstract theory.

The programming progression moves through NumPy and Pandas into Scikit-learn and PyTorch, matching the toolchain that data scientists and ML engineers actually use in production environments.

Career-changers, self-taught developers, and recent graduates use this roadmap to structure a 612 month self-study curriculum with clear milestones.

Unlike generic advice to 'take a Coursera course and do Kaggle', the roadmap provides specificityparticular chapters of specific textbooks, particular competition formats for different skill levels, particular interview preparation stepsthat turns vague career aspiration into an executable plan.

The GitHub-hosted format means the community can update recommendations as the AI tooling landscape shifts.