Machine Learning
From math foundations to deep learning, NLP, computer vision, and ML engineering.
Overview
A structured path from mathematical foundations to production ML systems. This roadmap covers everything from linear algebra and probability through classical algorithms, deep learning architectures, and the engineering practices needed to deploy models that actually work in production. Each phase builds on the previous one, so you develop genuine intuition for how algorithms work rather than treating them as black boxes.
Machine learning has moved from academic research into the core of nearly every major technology product. Recommendation engines at Netflix and Spotify, fraud detection at Stripe and PayPal, autonomous driving at Waymo, and search ranking at Google all depend on ML systems operating at massive scale. Companies are hiring ML engineers at every level, and the demand far outstrips supply — particularly for engineers who can bridge the gap between research prototypes and production systems that serve millions of requests per day.
What makes this roadmap effective is its balance between theory and practice. You will learn the mathematical foundations that interviewers test (gradient descent, bias-variance tradeoff, regularization) alongside the practical tools that employers need (scikit-learn for classical ML, PyTorch for deep learning, MLflow for experiment tracking). Every concept is paired with hands-on projects using real datasets from Kaggle and UCI, so you build portfolio pieces while learning. The roadmap also covers ML engineering — model serving, pipeline orchestration, monitoring — which most learning resources skip entirely.
By the end of this roadmap, you will be able to frame business problems as ML problems, select appropriate algorithms, train and evaluate models rigorously, deploy them as scalable APIs, and discuss the full ML lifecycle confidently in interviews at companies ranging from startups to FAANG.
Interview questions
- Do I need a PhD for ML roles?
- PyTorch or TensorFlow?
- How much math do I actually need?
- How do I get ML experience without a job?
- What are the most common ML interview topics?
- Classical ML or deep learning first?
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