Learning in public

AI, explained simply — one concept at a time.

I'm Josyula. I'm learning AI from scratch I'll explain every concept here as simply as I can, as I learn it. No jargon walls, read in order, mistakes included.

Start with Module 1 → 2 of 8 modules live · new ones as I learn them

The curriculum · 8 modules

✓ Module 01 ML Foundations Live What machine learning actually is, the three ways it learns, and the one trap that breaks every model: overfitting. ✓ Module 02 Classic Algorithms Live Regression, decision trees, random forests, boosting — and why the simplest model often wins. 3 Module 03 Deep Learning Next Neural networks and backprop — how everything scales up into the engine behind modern AI. 4 Module 04 Transformers & LLMs Soon Attention, tokenization, and how a model like ChatGPT is actually trained. 5 Module 05 Generative AI Soon GANs, diffusion, prompt engineering, RAG — and why models hallucinate. 6 Module 06 Data & Feature Engineering Soon The pipeline that feeds every model, and why messy data beats clever code. 7 Module 07 MLOps & Deployment Soon Serving, monitoring, and why models quietly rot in production. 8 Module 08 System Design & Interviews Soon Putting it together — and the questions that actually come up in GenAI interviews.

About this project

This is a learning log, not a textbook. I'm teaching each idea the way I wish it had been taught to me on day one — plainly, with a concrete example, and an honest note when something confused me. If you spot a mistake, tell me; corrections are the point.

Following along on X: @IamJosyula ·