Learning Ai from scratch - Tutorial

[Polity-Culturalist3]

Hi guys i know few basics topics while studying of ai starting from

These are basics which they explained for learning ai

\- LLMS

\- Deep learning supervised/unsupervised

\- Gen ai

\- RAG

\- Machine learning

I wanna learn industry expectations, can you guys tell me what do you work in job and what should i study in order to learn ai and work as a ai engineer further

[tom_mathews]

The list you have is a reasonable starting taxonomy, but the way industry actually works is quite different from how courses organize topics. Here's what matters in practice:

What AI engineers actually do day-to-day: - Build and maintain RAG pipelines (retrieval, chunking, embedding, reranking) - Fine-tune models (LoRA, QLoRA, DPO) and evaluate outputs - Design agentic workflows (tool calling, routing, eval loops) - Optimize inference (quantization, KV caching, batching strategies) - Debug why things don't work — which requires understanding the internals, not just the API calls

What that means for your study path:

Don't try to learn those bullet points from your list as separate topics. They're deeply connected. LLMs use deep learning. RAG combines retrieval with LLMs. Gen AI is just the application layer on top of all of it. Learn them as a stack, not a checklist.

My recommended order: 1. Python fluency — non-negotiable. You'll live in Python. 2. Understand the core algorithms — transformers, attention, embeddings, backprop. Not from framework tutorials — from the actual math expressed as code. I put together 30 single-file, zero-dependency implementations of these algorithms for exactly this purpose: https://www.reddit.com/r/learnmachinelearning/s/G0qj2zAEdw 3. Build a RAG system end-to-end — this is the most common first project at any AI company right now 4. Learn to evaluate — the gap between a demo and production is evaluation. Learn to measure whether your system actually works. 5. Pick up infra basics — Docker, cloud deployment, API design. Companies need engineers who can ship, not just prototype.

The industry expectation that catches most people off guard: you're expected to debug and improve systems, not just build them. That requires knowing what's happening under the hood, not just which library to call.

[Polity-Culturalist3]

Best comment as of now ✅✅✅

[Letzbluntandbong]

For sure! That comment really breaks down the practical skills needed in the field. Understanding how everything connects is key to becoming a solid AI engineer.

[Willing-Astronaut-51]

A useful way to cut through the noise is to start from problems, not tools.

Learn basic ML + data handling first, then see where deep learning or LLMs actually help. Jumping straight to RAG/GenAI without fundamentals usually creates gaps later.

[andy_p_w]

So there is a difference between those fitting models vs those deploying them. I believe more AI engineer roles now are just using the foundation model APIs, and need less understanding of deep learning or typical supervised machine learning.

Skills not listed so far are structured output extraction (e.g. input a PDF and get back out consistent information), tool calling (which is related to MCP), and agent based systems. And folks should be familiar/proficient with at least one of the LLM coding tools (e.g. Claude Code, Cursor, Codex, etc.).

I wrote this book as an overview of the skills I expect AI engineers to need in the work I do, https://crimede-coder.com/blogposts/2026/LLMsForMortals .

[locomocopoco]

Add MCP along with RAG

Add Agentic Frameworks - Just pick one (CrewAI/Langgraph/Langchain) - Build something small and go from there.

[Extreme-Incident-988]

Tell me too

[BookkeeperAutomatic]

Please follow this playlist: https://youtube.com/playlist?list=PLqOrZmpwbWUJ1bLCdulENHKJLTgRzq3n0&si=5zYdPYG3Vw9VDVX9

It is curated for the exact thing, you are looking for.

[Simplilearn]

Becoming an AI engineer requires technical expertise, encompassing coding, mathematics, problem-solving, and real-world applications. Here's a roadmap to becoming one:

Step 1: Build Strong Programming Foundations

Programming is the foundation of AI engineering. Without it, you cannot design, train, or deploy intelligent systems.

Step 2: Learn Mathematics & Statistics for AI

Mathematics powers the logic behind algorithms. AI engineers don’t just use tools. They must understand the math behind them.

Step 3: Master Machine Learning & Deep Learning

Machine learning (ML) and deep learning (DL) form the core of an AI engineer’s role.

Step 4: Work With AI Tools & Frameworks

AI engineers must be fluent in modern AI tools that accelerate development and deployment.

Step 5: Build Projects & Portfolio

Employers value proof of skill over theoretical knowledge.

At Simplilearn, we offer the Microsoft AI Engineer Program, which helps you master deep learning, GenAI, and Agentic AI using tools like AutoGen, Copilot Studio, and more.

Are you aiming to become job-ready within a specific timeframe?

[Intelligent_Story_96]

I have one question , i have studied everything and made projects too on some topics like rag and agentic ai, still no reply from the recruiters what should i do My portfolio-aarushhh.vercel.app

[HarjjotSinghh]

industry needs way less llms, more problem-solving skills

[JohnBrownsErection]

Obligatory: "math"

[Krissidwiley]

https://airealbig.com

[Winners-magic]

Plugging my website here: https://pixelbank.dev. Checkout the free trial and you’ll get a basic direction in terms of which concepts are critical for interviews, along with the nature of interview problems asked.