AI engineer progress

• 🧠AI Fundamentals

  A ground-up journey through AI, ML, deep learning, and neural networks.

  6/6
  1. 01What is AI? From Rules to Learning
  2. 02How Machines Learn
  3. 03Neural Networks from Scratch
  4. 04Backpropagation & Training
  5. 05CNNs & Computer Vision
  6. 06RNNs & Sequential Data
• 📐Essence of Linear Algebra

  A geometric, intuition-first walk through linear algebra, inspired by 3Blue1Brown's classic series. Each chapter builds a mental picture first, then the math. Chapters 1 to 3 are the fundamentals everything else stands on. Every article links the original video.

  6/16
  1. 01Vectors, What Even Are They?
  2. 02Linear Combinations, Span, and Basis Vectors
  3. 03Linear Transformations and Matrices
  4. 04Matrix Multiplication as Composition
  5. 05Three-Dimensional Linear Transformations
  6. 06The Determinant
  7. 07Inverse Matrices, Column Space, and Null Space
  8. 08Nonsquare Matrices as Transformations Between Dimensions
  9. 09Dot Products and Duality
  10. 10Cross Products
  11. 11Cross Products in the Light of Linear Transformations
  12. 12Cramer's Rule, Explained Geometrically
  13. 13Change of Basis
  14. 14Eigenvectors and Eigenvalues
  15. 15A Quick Trick for Computing Eigenvalues
  16. 16Abstract Vector Spaces
• 📈Essence of Calculus

  A geometric, intuition-first walk through calculus, inspired by 3Blue1Brown's classic series. Each chapter builds the picture before the formula: what a derivative really measures, why the chain rule looks the way it does, and how integration undoes it all. Chapters 1 to 4 are the gradient-and-chain-rule core that backprop is built on. Every article links the original video.

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  1. 01The Essence of Calculus
  2. 02The Paradox of the Derivative
  3. 03Derivative Formulas Through Geometry
  4. 04Visualizing the Chain Rule and Product Rule
  5. 05What's So Special About Euler's Number e?
  6. 06Implicit Differentiation, What's Going On Here?
  7. 07Limits, L'Hôpital's Rule, and Epsilon-Delta Definitions
  8. 08Integration and the Fundamental Theorem of Calculus
  9. 09What Does Area Have to Do with Slope?
  10. 10Higher Order Derivatives
  11. 11Taylor Series
  12. 12The Other Way to Visualize Derivatives
• 🤖Transformers & LLMs

  From attention mechanisms to text generation — how modern language models actually work under the hood.

  0/6
  1. 01Attention Is All You Need
  2. 02The Transformer Architecture
  3. 03Tokenization
  4. 04Pre-Training LLMs
  5. 05GPT, LLaMA & Model Families
  6. 06Text Generation Strategies
• 🎯Post-Training & Alignment

  How base models become helpful assistants — supervised fine-tuning, RLHF, efficient adapters, and how we measure whether any of it works.

  0/4
  1. 01Supervised Fine-Tuning (SFT)
  2. 02RLHF & Reward Models
  3. 03Efficient Fine-Tuning
  4. 04Evaluation & Benchmarks
• 🔍RAG, Prompting & Applications

  From crafting the perfect prompt to building retrieval-augmented systems — everything you need to start building real things with LLMs.

  4/5
  1. 01Prompt Engineering
  2. 02Embeddings & Vector Search
  3. 03RAG: Retrieval-Augmented Generation
  4. 04Building a Chatbot
  5. 05Structured Output & Tool Schemasdraft
• 🤖Agents & Reasoning

  LLMs that can think, plan, and act — from simple tool-calling to multi-step reasoning agents that solve complex problems on their own.

  1/5
  1. 01AI Agents: Beyond Chat
  2. 02Agent Patterns
  3. 03ReACT & Multi-Step Agents
  4. 04Reasoning Models
  5. 05MCP: Model Context Protocoldraft
• 🎨Multimodal & Generative AI

  How AI creates images, videos, and more — from diffusion models and Stable Diffusion to the cutting edge of generative AI.

  0/4
  1. 01How Image Generation Works
  2. 02Text-to-Image (Stable Diffusion)
  3. 03Text-to-Video & Beyond
  4. 04The AI Landscape: What's Next
• 🚀Production AI Engineering

  Bridge the gap from understanding AI to shipping it — build, serve, evaluate, and maintain AI systems in production like a real AI engineer.

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  1. 01Your First AI App with HuggingFace
  2. 02Fine-Tuning in Practice with PyTorch
  3. 03LangChain & LlamaIndex: Build with LLMs
  4. 04Serving AI Models: From Notebook to API
  5. 05Cloud AI: AWS, GCP & Azure for AI Engineers
  6. 06MLOps: Shipping AI Like Software
  7. 07Evaluating LLMs at Scale
  8. 08Cost, Latency & Context Management
  9. 09LLM Observability: Tracing with Langfuse & OpenTelemetrydraft
  10. 10Caching for LLM Apps: Prompt, Semantic & KV Cachedraft
  11. 11AI Security: Prompt Injection to Tenant Isolationdraft

Shares Essence of Linear Algebra, Essence of Calculus, AI Fundamentals, Transformers & LLMs, Production AI Engineering with the path above. Listed once.

• 🎲Statistics & Probability

  The language of uncertainty that every ML model speaks — probability, distributions, Bayes, hypothesis testing, and the bias-variance tradeoff. The screening layer of every ML engineer interview.

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  1. 01Probability: The Language of Uncertainty
  2. 02Random Variables & Distributions
  3. 03Bayes' Theorem & Conditional Probability
  4. 04Descriptive Statistics & Sampling
  5. 05Hypothesis Testing & Confidence Intervals
  6. 06Maximum Likelihood Estimation
  7. 07The Bias-Variance Tradeoff
• 📊Classical Machine Learning

  The models that run most production ML today — regression, trees, gradient boosting, clustering, and how to evaluate them honestly. What fintech and enterprise ML teams actually ship.

  0/10
  1. 01Linear Regression: Where ML Begins
  2. 02Logistic Regression & Classification
  3. 03Decision Trees
  4. 04Ensembles: Random Forest to XGBoost
  5. 05SVM & k-Nearest Neighbors
  6. 06Clustering: k-Means & Beyond
  7. 07PCA & Dimensionality Reduction
  8. 08Feature Engineering
  9. 09Model Evaluation: Precision, Recall & ROC-AUC
  10. 10Cross-Validation & Imbalanced Data
• 🏦Applied ML Systems

  Real ML systems that companies hire for — fraud detection, credit scoring, recommenders, and forecasting. The problems fintech and government AI teams solve every day, plus how to reason about them in system design interviews.

  0/5
  1. 01Fraud Detection with ML
  2. 02Credit Scoring & Risk Models
  3. 03Recommender Systems
  4. 04Time-Series Forecasting
  5. 05The ML System Design Interview