AI Learning Handbook
AI, Systems, and Structured Thinking.
A structured space for mastering artificial intelligence — deliberately, deeply, and without noise.
How to Use This Site
- 1.Start with the Roadmap.
- 2.Study phase by phase.
- 3.Use articles to deepen understanding.
- 4.Build and iterate.
Why This Exists
The emphasis here is on depth over noise. Structured mastery over scattered tutorials. Long-term learning over quick wins. Clarity over speed. This site exists as a place to document that approach — deliberately, and without the usual hype.
AI Mastery Roadmap
Foundation
4–6 weeks · ~40–50 hours
Get from zero to running Python and writing scripts. Variables, loops, functions, and a clear idea of what machine learning is.
Key topics
- Install Python 3, VS Code, and set up your environment
- Variables, types, conditionals, loops, functions
- Lists, dicts, and basic data structures
- Small projects (calculator, text game, data parsing)
- Intro: supervised vs unsupervised, training vs inference
Math + Machine Learning
Months 1–3 · ~130 hours
Linear algebra, probability, and calculus foundations. Then ML from scratch: linear regression, logistic regression, basic optimization. No black boxes.
Key topics
- Linear algebra: vectors, matrices, dot products, matrix multiplication
- Probability: distributions, Bayes, expectation, variance
- Calculus: derivatives, gradients, chain rule
- Andrew Ng Machine Learning (Coursera) or equivalent
- Implement linear regression and logistic regression from scratch
- Gradient descent and normal equation
Deep Learning
Months 4–6 · ~130 hours
Neural networks, backpropagation, CNNs, attention, and transformers. Build implementation skills, use PyTorch/TensorFlow, and start reading papers.
Key topics
- Neural nets: forward pass, backpropagation, activation functions
- CNNs: convolutions, pooling, ResNet-style architectures
- Attention mechanism and transformer architecture
- PyTorch or TensorFlow — build and train models
- Paper reading: one paper per 2 weeks, implement key ideas
- Stanford CS231n and DeepLearning.AI specialization
Specialization & Systems
Months 7–9 · ~130 hours
Deep dive into one track (NLP, CV, or RL). Add learning theory, deployment basics, and build a substantial project with clear documentation.
Key topics
- One track: NLP, Computer Vision, or Reinforcement Learning
- Learning theory: bias-variance, overfitting, generalization, regularization
- Systems: model deployment, FastAPI, basic MLOps
- Paper reading: 2–3 papers per month in your track
- Substantial project: replication or novel implementation
- Write a clear technical report (5–10 pages)
Advanced Mastery
Months 10–12 · ~130 hours
Paper writing, rigorous replications, and advanced topics. Consolidate knowledge and produce work that demonstrates mastery. Capstone project.
Key topics
- Rigorous replication of 1–2 key papers in your specialization
- Paper writing: clear problem, method, experiments, analysis
- Consolidation: review full curriculum, fill gaps
- Capstone: one major project or replication
- Documentation, code quality, reproducibility
- Optional: contribute to OSS, write blog posts
Latest Writing
Depth vs Hype: The Real Skill Gap in AI
An analytical look at tool obsession, the illusion of productivity, and what genuine depth in AI actually requires for engineers serious about mastery.
February 16, 2026
How I Structure My AI Study Weeks
A repeatable study system for serious AI learners: weekly architecture, daily splits, reinforcement practices, and feedback loops.
February 16, 2026
Why Most AI Roadmaps Fail (And How to Fix Yours)
A technical-reflective examination of roadmap overload, structural flaws, and evidence-based design for serious AI learners pursuing long-term mastery.
February 16, 2026