From foundations
to frontier AI.

How This Industry Actually Works

An orientation to software and AI for anyone walking in — student, fresher, or someone with years of experience who never learned the process side. What AI is, how companies are structured, who does what, how software actually gets built and shipped, and the vocabulary everyone assumes you already know.

Python for Machine Learning

Start from zero and become an expert. 55 lessons including 10 hands-on mini-projects (Mad Libs, FizzBuzz, Number Guess, Tip Calculator, Word Counter, Todo App, Bank Account, more). Variables, loops, functions, data structures, OOP, decorators, async, algorithms, FastAPI, and a capstone where you call your first LLM API.

Query Any Database

From your first SELECT to window functions, data modeling, and interview patterns. Practice with a built-in SQL playground running real queries in your browser.

The Math You Actually Need

Vectors, matrices, derivatives, probability, optimization, and stochastic calculus — from zero to the math you need to read modern ML papers. 17 lessons, foundations through advanced.

Working with Real Data

From raw chaos to ML-ready features. Pipelines, ingestion, EDA, cleaning, outliers, encoding, feature engineering, selection, dimensionality reduction, time series, imbalance, augmentation, splits, cross-validation, and drift — 15 lessons covering the 80% of ML work that decides whether your model succeeds.

Your First ML Algorithms

From linear regression to XGBoost, SHAP, discriminant analysis, and causal inference. 24 lessons covering the algorithms, diagnostic skills (bias-variance, learning curves), feature engineering, time-series, recommender systems, dimensionality reduction, and the practical mastery (calibration, hyperparameter tuning, model interpretation) that turn a textbook reader into a working ML engineer.

How Neural Networks Think

From tensors and autograd to ResNet, transformers, GANs/VAEs, ViTs, object detection, diffusion models, graph neural networks, self-supervised pretraining, mixture-of-experts, state-space-models/Mamba, flow matching, and FSDP-distributed training. 27 lessons covering the foundations, the architectures, the generative stack, the post-transformer family, and the production-scale tricks that turn a notebook prototype into a 70B-parameter model.

How ChatGPT Works

From tokenization and embeddings through GPT/BERT/T5, RLHF, FlashAttention, MoE, Mamba, ViT, mechanistic interpretability, tool-calling, structured generation, knowledge editing, synthetic data, modern safety/red-teaming, distributed training, pretraining data pipelines, and 2026 frontier evals. 35 lessons covering the foundations, the architectures, the alignment pipeline, the efficiency stack, and the deployment reality of 2026 LLMs.

How AI Creates Images and Music

VAEs, GANs, normalizing flows, score-based models, diffusion, flow matching, latent diffusion, ControlNet, consistency models, diffusion-DPO/RLHF, video, 3D, audio. 19 lessons covering the full generative-AI stack — from theory to FLUX/Sora/SDXL Turbo production.

AI That Learns by Playing

From MDPs and Q-learning to AlphaGo/MuZero, multi-agent self-play, offline RL, hierarchical RL/options, and RLHF/GRPO for LLMs. 18 lessons covering classical foundations, deep RL, search-and-learning, and the 2024-2026 frontier (DeepSeek-R1, AlphaProof, AlphaStar).

Give AI a Knowledge Base

From embeddings and chunking through hybrid retrieval, reranking, GraphRAG, multimodal RAG, agentic RAG, evaluation, and production deployment. 16 lessons covering the full Perplexity/Glean-style production stack.

AI That Plans and Acts

From agent loops and tool use through MCP, ReAct, Reflexion, agent frameworks, multi-agent orchestration, computer-use agents, and Devin-style capstone. 17 lessons covering Anthropic's workflow patterns, CrewAI/AutoGen/LangGraph, SWE-bench-driven code agents, and production hardening.

From Notebook to Production

From data pipelines through serving, multi-model GPU infrastructure, FinOps for AI, EU AI Act compliance, and incident response. 23 lessons covering the production engineering that turns a working prototype into a reliable system.

Master Claude Code

Zero to expert Claude Code. Install, CLAUDE.md, the .claude/ ecosystem, permissions, slash commands, hooks, subagents, MCP, git workflows, code review, web grounding, background tasks, and team-scale deployment.

Your Career in AI

Resume writing, interview prep, placement strategy, AI safety as a career path, real ML production-disaster case studies, and the interview anti-patterns that sink otherwise-qualified candidates. 12 lessons covering career-specific guides for breaking into AI engineering.

Ship Inside the Customer's World

The role Palantir invented and the AI labs now hire for aggressively: engineers who embed with a customer, find the real problem, and ship production software inside someone else's environment. The engagement lifecycle, discovery, scoping, rapid prototyping with evals, messy data integration, locked-down deployment environments, and the craft of turning bespoke work into product.