If you're learning machine learning, don't spend money on courses. These are the free resources I would recommend to you. which helps you to learn from the basics. Python Programming — Corey Schafer youtube.com/playlist?list=PL… Essence of Calculus — 3Blue1Brown youtube.com/playlist?list=PL… Essence of Linear Algebra — 3Blue1Brown youtube.com/playlist?list=PL… Probability Bootcamp — Steve Brunton youtube.com/playlist?list=PL… Statistics Bootcamp — Steve Brunton youtube.com/playlist?list=PL… Statistics — StatQuest youtube.com/playlist?list=PL… Machine Learning — Andrew Ng youtube.com/playlist?list=PL… Deep Learning — Krish Naik youtube.com/playlist?list=PL… Natural Language Processing — Krish Naik youtube.com/playlist?list=PL… Neural Networks: Zero to Hero — Andrej Karpathy youtube.com/playlist?list=PL… LLMs — Stanford CME295 youtube.com/playlist?list=PL… RAG youtu.be/sVcwVQRHIc8?si=LmmI… Ultimate MLOps Course — Vikash Das youtube.com/playlist?list=PL… ML system design youtube.com/playlist?list=PL… ML Interview Preparation youtube.com/playlist?list=PL… What you'll learn: ✓ Python ✓ Calculus ✓ Linear Algebra ✓ Probability ✓ Statistics ✓ Machine Learning ✓ Deep Learning ✓ NLP ✓ LLMs ✓ RAG ✓ MLOps ✓ ML system design ✓ ML Interviews Save this resources for later. 🔖

One line stood out to me: “I want to go back to basics and think.” In an industry racing toward AI agents and automation, that’s a powerful statement. Do you agree with him?

“Do not try to do everything. Do one thing well.” — Steve Jobs

Can you trust your agentic systems with the measurements you track? In my latest Newsletter episode I dig deeper into why and how agentic system monitoring differs from regular web services. I.e. what kind of additional metrics you should track as an AI Engineer building on top of LLMs. For me personally it helps to group metrics by the question they answer. Five questions cover most of what goes wrong in production: ➡️ Is it fast? ➡️ Can it scale? ➡️ Is it correct? ➡️ Does it hold up? ➡️ When there is an agent in the loop, how does it behave? Find the episode here: newsletter.swirlai.com/p/sto… In the article I expand on each question and nuances you need to have in mind when instrumenting your applications. Don’t miss this episode if you are serious about bringing your AI apps to production.

Most RAG systems fail for 3 reasons: 1. Poor retrieval 2. Weak evaluation 3. Bad architecture These 3 courses directly address each problem: → Fine-Tuning LLMs → Enterprise RAG Architecture → Retrieval & Evaluation Links 👇 1. maven.com/p/787a2f/rag-and-l… 2. maven.com/p/f4bbc2/building-… 3. maven.com/p/fae749/modern-in…

If I had to learn machine learning from scratch in 2026, these would be my top 7 GitHub repositories: 1. Hands-On Machine Learning github.com/ageron/handson-ml… → The gold standard for practical ML. 2. ML From Scratch github.com/eriklindernoren/M… → Implement ML algorithms from scratch and understand how they work under the hood. 3. Applied ML github.com/eugeneyan/applied… → Real-world lessons on recommendation systems, search, ranking, experimentation, and ML engineering. 4. Machine Learning Refined github.com/neonwatty/machine… → One of the best resources for understanding ML mathematics and intuition. 5. Made With ML github.com/GokuMohandas/Made… → Learn production ML, MLOps, experimentation, and deployment. 6. FastAI github.com/fastai/fastai → One of the best resources for practical deep learning. 7. RAG Techniques github.com/NirDiamant/RAG_Te… → Master modern GenAI, retrieval-augmented generation, and evaluation. Bookmark this. 🔖

Every day I see: “Should I learn this?” “Is this worth it?” “Which tool is better?” Meanwhile, someone else is already building.

If you want to get good at AI engineering (in 2026), learn these concepts: 1 LLM Evals Explained ↳ newsletter.systemdesign.one/… 2 Design Knowledge Q & A System ↳ newsletter.systemdesign.one/… 3 How OpenClaw Works ↳ newsletter.systemdesign.one/… 4 AI Agent Workflow ↳ newsletter.systemdesign.one/… 5 How MCP Works ↳ newsletter.systemdesign.one/… 6 Design AI Chat Assistant ↳ newsletter.systemdesign.one/… 7 How RAG Works ↳ newsletter.systemdesign.one/… 8 Agentic Patterns Explained ↳ newsletter.systemdesign.one/… 9 AI Coding Workflow 101 ↳ newsletter.systemdesign.one/… 10 Machine Learning System Design 101 ↳ newsletter.systemdesign.one/… 11 Multi-Agent Architecture Explained ↳ newsletter.systemdesign.one/… 12 How AI Agents Work ↳ newsletter.systemdesign.one/… 13 How Vector Databases Work ↳ newsletter.systemdesign.one/… 14 AI Agents: Memory, State & Consistency ↳ newsletter.systemdesign.one/… 15 AI Agents Design ↳ newsletter.systemdesign.one/… 16 Context Engineering 101 ↳ newsletter.systemdesign.one/… 17 What is Reinforcement Learning ↳ newsletter.systemdesign.one/… 18 LLM Concepts - A Deep Dive ↳ newsletter.systemdesign.one/… What else should make this list? === 👋 PS - Want my System Design Playbook (for free)? Join my newsletter with 201K software engineers now: → newsletter.systemdesign.one/… === 💾 Save & RT to help others get good at AI engineering. 👤 Follow @systemdesignone turn on notifications.

Good morning, everyone

1822: First Millionaire (John Jacob Astor) 1916: First Billionaire (John D. Rockefeller) 2026: First Trillionaire (Elon Musk) ????: First Quadrillionaire Who do you think gets there first? 👇

Stop learning everything. Learn what companies hire for…

To build production AI systems, you need: • Software Engineering • Data Engineering • System Design • AI/ML

As a AI/ML Engineer, it will be good if you have an understanding of the below 50 topics 👇 1. Python 2. NumPy 3. Pandas 4. Data Cleaning 5. Feature Engineering 6. Statistics 7. Probability 8. Linear Algebra 9. Calculus Basics 10. SQL 11. Machine Learning Fundamentals 12. Supervised Learning 13. Unsupervised Learning 14. Ensemble Methods 15. XGBoost 16. LightGBM 17. CatBoost 18. Model Evaluation 19. Hyperparameter Tuning 20. Cross Validation 21. Deep Learning 22. Neural Networks 23. CNNs 24. RNNs 25. LSTMs 26. Transformers 27. Attention Mechanism 28. Transfer Learning 29. Fine-Tuning 30. Model Quantization 31. NLP 32. Embeddings 33. Vector Databases 34. Semantic Search 35. RAG 36. Graph RAG 37. Prompt Engineering 38. AI Agents 39. Multi-Agent Systems 40. Tool Calling 41. MLOps 42. MLflow 43. Docker 44. Kubernetes 45. FastAPI 46. CI/CD 47. Monitoring & Observability 48. AWS/GCP/Azure 49. Distributed Training 50. LLM Evaluation & Guardrails Most AI Engineers stop at model training. The highest-paid ones know how to ship models into production.

5 mistakes I made while learning ML: 1. Ignored SQL 2. Ignored statistics 3. Focused only on models 4. Avoided system design 5. Waited too long to build projects

If coding is no longer worth learning, why are companies still using it to filter candidates?

Check out this repo guys

My friend interviewed for a Machine Learning role this morning. 3 years of ML experience. He expected questions on: LLMs. RAG. Fine-tuning. AI Agents. The interviewer asked: Round 1 - Solve a DSA problem. Round 2- Explain p-values. Explain Hypothesis testing. Design an A/B test. Baye’s theorem. Sometimes I think ML engineers underestimate how important the fundamentals still are.

ANTHROPIC DROPPED FREE COURSES FOR CLAUDE video lessons quizzes on: ▪️ Claude API (84 lectures, 8hrs) ▪️ Claude Code ▪️ MCP ▪️ AI Fluency free, self-paced, certifiable