AI Engineering From Scratch
Learn it. Build it. Ship it for others.
Install / Use
/learn @rohitg00/AI Engineering From ScratchREADME
84% of students already use AI tools. Only 18% feel prepared to use them professionally. This course closes that gap.
260+ lessons. 20 phases. ~290 hours. From linear algebra to autonomous agent swarms. Python, TypeScript, Rust, Julia. Every lesson produces something reusable -- prompts, skills, agents, MCP servers.
You don't just learn AI. You learn AI with AI. Then you build real things. Then you ship tools others can use.
| | Traditional Courses | This Course | |---|---|---| | Scope | One slice (NLP or Vision or Agents) | Everything: math, ML, DL, NLP, vision, speech, transformers, LLMs, agents, swarms | | Languages | Python only | Python, TypeScript, Rust, Julia | | Output | "I learned something" | A portfolio of tools, prompts, skills, and agents you can install | | Depth | Surface-level or theory-heavy | Build from scratch first, then use frameworks | | Format | Videos you watch | Runnable code + docs + web app + AI-powered quizzes | | Learning style | Passive consumption | AI-native: use Claude Code skills to test yourself as you go |
🧠 AI-Native Learning
This isn't a course you watch. It's a course you use with your AI coding agent.
Learn with AI, not just about AI
# Find where to start based on what you already know
/find-your-level
# Quiz yourself after completing a phase
/check-understanding 3
# Every lesson produces a reusable artifact
ls phases/03-deep-learning-core/05-loss-functions/outputs/
# prompt-loss-function-selector.md
# prompt-loss-debugger.md
Built-in Claude Code Skills
| Skill | What it does |
|-------|-------------|
| /find-your-level | 10-question quiz that maps your knowledge to a starting phase and builds a personalized path with hour estimates |
| /check-understanding <phase> | Per-phase quiz (8 questions) with feedback and specific lessons to review |
Every Lesson Ships Something
Other courses end with "congratulations, you learned X." Our lessons end with a reusable tool:
- Prompts -- paste into any AI assistant to get expert-level help on the topic
- Skills -- install into Claude Code, Cursor, or any coding agent
- Agents -- deploy as autonomous workers
- MCP servers -- plug into any MCP-compatible AI app
277-term searchable glossary. Full lesson catalog. ~290 hours of content with per-lesson time estimates. Browse the website →
The Journey
<table> <tr><td>Phase 0: Setup & Tooling 12 lessons
Get your environment ready for everything that follows.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | Dev Environment | Build | Python, Node, Rust | | 02 | Git & Collaboration | Learn | -- | | 03 | GPU Setup & Cloud | Build | Python | | 04 | APIs & Keys | Build | Python, TS | | 05 | Jupyter Notebooks | Build | Python | | 06 | Python Environments | Build | Python | | 07 | Docker for AI | Build | Python | | 08 | Editor Setup | Build | -- | | 09 | Data Management | Build | Python | | 10 | Terminal & Shell | Learn | -- | | 11 | Linux for AI | Learn | -- | | 12 | Debugging & Profiling | Build | Python |
</td></tr> </table> <details id="phase-1"> <summary><strong>Phase 1: Math Foundations</strong> <code>22 lessons</code> <em>The intuition behind every AI algorithm, through code.</em></summary>| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | Linear Algebra Intuition | Learn | Python, Julia | | 02 | Vectors, Matrices & Operations | Build | Python, Julia | | 03 | Matrix Transformations & Eigenvalues | Build | Python, Julia | | 04 | Calculus for ML: Derivatives & Gradients | Learn | Python | | 05 | Chain Rule & Automatic Differentiation | Build | Python | | 06 | Probability & Distributions | Learn | Python | | 07 | Bayes' Theorem & Statistical Thinking | Build | Python | | 08 | Optimization: Gradient Descent Family | Build | Python | | 09 | Information Theory: Entropy, KL Divergence | Learn | Python | | 10 | Dimensionality Reduction: PCA, t-SNE, UMAP | Build | Python | | 11 | Singular Value Decomposition | Build | Python, Julia | | 12 | Tensor Operations | Build | Python | | 13 | Numerical Stability | Build | Python | | 14 | Norms & Distances | Build | Python | | 15 | Statistics for ML | Build | Python | | 16 | Sampling Methods | Build | Python | | 17 | Linear Systems | Build | Python | | 18 | Convex Optimization | Build | Python | | 19 | Complex Numbers for AI | Learn | Python | | 20 | The Fourier Transform | Build | Python | | 21 | Graph Theory for ML | Build | Python | | 22 | Stochastic Processes | Learn | Python |
</details> <details id="phase-2"> <summary><strong>Phase 2: ML Fundamentals</strong> <code>18 lessons</code> <em>Classical ML - still the backbone of most production AI.</em></summary>| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | What Is Machine Learning | Learn | Python | | 02 | Linear Regression from Scratch | Build | Python | | 03 | Logistic Regression & Classification | Build | Python | | 04 | Decision Trees & Random Forests | Build | Python | | 05 | Support Vector Machines | Build | Python | | 06 | KNN & Distance Metrics | Build | Python | | 07 | Unsupervised Learning: K-Means, DBSCAN | Build | Python | | 08 | Feature Engineering & Selection | Build | Python | | 09 | Model Evaluation: Metrics, Cross-Validation | Build | Python | | 10 | Bias, Variance & the Learning Curve | Learn | Python | | 11 | Ensemble Methods: Boosting, Bagging, Stacking | Build | Python | | 12 | Hyperparameter Tuning | Build | Python | | 13 | ML Pipelines & Experiment Tracking | Build | Python | | 14 | Naive Bayes | Build | Python | | 15 | Time Series Fundamentals | Build | Python | | 16 | Anomaly Detection | Build | Python | | 17 | Handling Imbalanced Data | Build | Python | | 18 | Feature Selection | Build | Python |
</details> <details id="phase-3"> <summary><strong>Phase 3: Deep Learning Core</strong> <code>13 lessons</code> <em>Neural networks from first principles. No frameworks until you build one.</em></summary>| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [The Perceptron: Where It
