AI Engineering Course: Fundamentals to Deployment

Most AI tutorials online are either a 15-minute surface skim or a 3-week paper dissection. Nothing in between. For two years, we have been building the missing middle: Pods — short, rigorous modules that take you from fundamentals to practicals to research in a single sitting. Today, we are adding the layer that changes how these are taught: video lessons, recorded personally by the founders. Every pod on the platform now has a deep-dive video where one of us — Raj, Rajat, or Sreedath — walks through the concept, the math, and the code we wrote ourselves. No external narrator. No cut-and-paste voiceover. Just the author of the article, explaining the ideas in their own voice. Three things make these different from anything else you'll watch this week: (1) Taught by the people who wrote the article and built the notebook. You hear the author’s reasoning, not a presenter’s summary. (2) Every video is chapter-seekable — skip to the exact minute you need. (3) A new one drops every week. We’re recording through all 13 courses on the platform. We have launched with 11 videos covering Reinforcement Learning and Context Engineering — including RLHF, Value Functions, Q-Learning, Policy Gradients, DQN Atari, Building Reasoning Models, RAG Systems, Prompt Design, Memory Architectures, and more.

Just wrapped up the AI Engineer Core Track on Udemy Business, led by Ed Donner. Really enjoyed how this course is put together — it balances core concepts with hands‑on, practical implementation, which made the learning stick. Nothing felt purely theoretical for the sake of it. Covered topics like: - LLM engineering basics - Retrieval‑Augmented Generation (RAG) - QLoRA fine‑tuning - Agent design and orchestration What stood out for me was the focus not just on how to build AI systems, but why certain design and architectural choices matter, especially when you think about real‑world scale and production use. If you’re looking to get deeper into modern AI engineering with strong practical grounding, this is a solid track: 🔗 https://lnkd.in/gvZ5YeMK Looking forward to applying these learnings in real projects. #AIEngineering #LLMs #GenerativeAI #RAG #AIAgents #AgenticAI #ContinuousLearning #UdemyBusiness  

🚀 Before I started learning AI architecture, I didn’t open a single tutorial. 🧠 I built my own knowledge roadmap first. Because jumping into random videos = confusion. Understanding the big picture first = clarity. So I mapped out everything I need to learn: ✔️ LLM Fundamentals — how models actually work 💰 Tokens, Cost & Performance — what really impacts systems 🧩 Prompt Engineering — structure, not guesswork 🔍 Embeddings, Vector DB & RAG — the real AI pipeline 🏗️ AI System Design — from components → production This isn’t about collecting information. It’s about learning in a way that actually leads somewhere. 📌 Sharing this to stay accountable — and for anyone trying to learn without wasting time. https://lnkd.in/gu4BctaD

There's a vast difference between just using AI and actually building with it. As an AI instructor and software engineer, the question I get most is: where do I actually learn to build? Not theory—actual automations, agents, and tools that save hours and make you harder to replace at work. I partnered with Leland on their AI Builder Program because I think it's a genuine answer to that. 5 levels, taught by experts from Google, OpenAI, and Meta. Level 1 kicks off April 21 (tomorrow!) and the rest are through the summer. Use code LELANDAI50-ISHA on ai.joinleland.com for a 50% discount. Hope to see you there! DM me if you want to know more before signing up.

If you're a software engineer trying to break into the AI world, this is your sign to check out Anthropic's free course library. They've put together structured learning paths covering: → How to build with the Claude API (84 lectures!) → Model Context Protocol (MCP) — the new standard for connecting AI to real-world tools → Claude Code — agentic coding workflows → AI Fluency — for understanding how to actually work alongside AI As someone just starting out, the hardest part isn't the concepts — it's knowing *where to start*. These courses solve that. Video lessons + quizzes + a clear progression path. No gatekeeping. No overpriced bootcamps. Just structured learning from the team building one of the most advanced AI systems in the world. Link in the comments 👇 #SoftwareEngineering #AI #Anthropic #LearningAndDevelopment #FreshersGuide

Spent some time this week trying to figure out which AI model is actually the best right now, and what I realized is that the leaderboards have basically stopped being useful. Every new release claims to top SWE-bench or MMLU or whatever, and on paper the gap between frontier and open-source has nearly closed, but the more you read into how these scores are produced the more it feels like the models are getting trained to ace the test rather than do the work. When I tried some of the models they claim to be on par with Opus, I ended up pretty disappointed (for coding at least). The problem is that benchmarks are also getting harder to design, because anything you publish online ends up in the next training run, so probably the best way to know is to just test it yourself using your own benchmark.

A few months ago, I was the person nodding along when someone said "transformer architecture." I was learning AI and drowning in terminology — not because the concepts were too hard, but because every resource I found assumed I was already an engineer. I kept thinking: I can't be the only person hitting this wall. I study how people learn, not how to build software. So I built the course I couldn't find. AI Decoded is 6 free lessons, each about 5 minutes. It covers the terminology that kept tripping me up: AI vs. machine learning, how LLMs actually work, why they hallucinate, what prompt engineering really means, and what agents and RAG actually are. Each lesson has clear objectives, analogies that actually stick, an interactive diagram, and a short quiz. No coding. No login. If you're in the same place I was, this is for you: https://lnkd.in/gqWSwqiY #AILiteracy #LearningDesign #ArtificialIntelligence #AIEducation

Okay so I've been heads down for the past few weeks in an AI Engineering bootcamp and I finally feel like things are clicking. Here's what i covered — Started with the fundamentals: problem framing, setting up environments, and understanding why RAG even exists before touching any code. Then built a minimal end-to-end RAG app from scratch — document loading, chunking, generating embeddings, storing them in a vector DB, and retrieving relevant context to prompt an LLM. Sounds simple when you list it out, but debugging a retrieval pipeline at midnight hits different. Week 2 was all about making retrieval actually good. Hybrid search (BM25 + vector), reranking, context engineering, smarter chunking strategies, and RAG evaluation. This is where I realized most RAG tutorials skip the hard part — getting relevant results consistently is non-trivial. Then we got into Agentic RAG. Agents, tool calling, planning, function schemas, guardrails — and finally combining it all into a single-agent workflow. This is the part that genuinely excited me. The jump from "retrieve and answer" to "reason, act, and decide" is massive. Still a lot to build and break, but the foundation feels solid now. If you're exploring AI engineering, honestly just start building. The messy middle is where the learning happens. #AIEngineering

Google AI Professional Certificate ✅ 7 courses. 20+ AI artifacts built. Vibe coded a custom AI solution. Courses: AI Fundamentals Prompt Engineering AI for Data Analysis AI for Research Content Creation with AI App Building with AI Vibe Coding

Free LLM Mini Course: Learn • Code • Build AI 🎥 👉 Start the course: https://lnkd.in/gGv2idM2 Master the fundamentals of LLMs 🧠 with a perfect blend of concepts + hands-on code demos 💻 across 3 short videos (≈30 mins). Build practical AI applications, learn fine-tuning & customization, and gain real-world skills — all for FREE 🎯. 👉 Subscribe to Kanayi’s AI Lab 🔔 https://lnkd.in/gWZKjHiz for more advanced courses and hands-on content to shape your AI career 📈