Bookmark This: The Free AI Learning Path for 2026

A practical guide to the best free AI courses, tools, and learning paths from the companies and universities shaping the field.

I keep seeing the same problem with AI learning.

There are courses everywhere.

Google has courses. Microsoft has courses. OpenAI has an academy. Anthropic has Claude courses. NVIDIA has training. MIT and Harvard have free university material. Hugging Face has open courses. YouTube has world-class lectures.

But a long list of links is not the same thing as a learning path.

So I made this as a map.

Not by company.

By what you actually need to learn first.

Start with AI basics. Then learn LLMs. Then prompting. Then agents. Then automation, RAG, MCP, Claude Code, and production AI systems.

I have also added my own Opinion AI guides after each section. The free resources below are the raw learning material. My guides are where I try to organize, simplify, and connect the best parts into one practical system.

A $10 subscription gives you access to 74 practical AI guides covering agents, Claude, Codex, MCP, RAG, automations, coding, workflows, business use cases, and the parts of AI that are actually useful for work.

1. Learn AI basics first

Start here if you are new.

This layer is not about coding. It is about understanding what AI is, where it helps, where it fails, and how to use it without getting lost in hype.

Best free resources

My suggestion is simple.

Take Elements of AI first. Then Google AI Training. Then Microsoft AI for Beginners if you want more depth.

Opinion AI guide to read next

If you want the simplified map of the main ideas, read:

2. Learn how LLMs work

LLMs are the engine behind ChatGPT, Claude, Gemini, DeepSeek, Llama, and many AI tools.

You do not need to understand every math detail.

But you should know tokens, context windows, transformers, training, fine-tuning, reasoning, hallucination, and why a model sometimes sounds confident while being wrong.

Best free resources

This is the layer that makes everything else easier.

Once you understand LLMs, prompting makes more sense. Agents make more sense. RAG makes more sense.

Opinion AI guide to read next

3. Learn prompting as work design

Prompting is not magic wording.

It is task design.

Good prompting means giving the model context, role, examples, constraints, output format, and a way to check its own work.

This is the skill every reader should learn, even if they never write code.

Best free resources

The simple rule is this.

Do not ask AI to guess your task.

Give it the work like you would give it to a smart assistant on the first day.

Opinion AI guide to read next

4. Learn AI agents

This is the most important category right now.

A chatbot answers.

An agent works through steps.

It can plan, use tools, call APIs, search files, run code, check results, and continue the task.

This is where AI starts moving from conversation to work.

Best free resources

Start small.

Build one agent that does one job.

Then add tools.

Then add memory.

Then add evaluation.

Opinion AI guide to read next

5. Learn automation and workflows

This is where AI becomes useful for business.

Not in a demo.

In daily work.

Research flows. Email flows. Lead flows. Content flows. Reporting flows. Customer support flows. Invoice checks. Meeting summaries. Internal dashboards.

Agents need workflows around them.

Best free resources

Learn automation early.

It teaches you how work actually moves from one step to another.

Opinion AI guide to read next

6. Learn Claude and Claude Code

Claude is becoming one of the most useful AI systems for writing, research, coding, documents, analysis, and agentic work.

Claude Code is even more important because it shows where coding is going.

The old way was writing every line yourself.

The new way is setting context, giving rules, checking changes, and letting the coding agent work inside a clear system.

Best free resources

If you write, research, build, or manage projects, learn Claude seriously.

It is not only a chatbot.

It can become a work system.

Opinion AI guide to read next

7. Learn MCP and tool-connected AI

MCP means Model Context Protocol.

Simple meaning: it helps AI systems connect to tools, files, databases, apps, and services in a cleaner way.

This matters because AI is leaving the empty chat box.

The next layer is connected AI.

AI that can use tools.

AI that can read your files.

AI that can work inside your software.

AI that can call the right system at the right time.

Best free resources

Learn MCP because it is part of the agent infrastructure story.

Not every reader needs to build MCP servers today.

But every serious AI learner should understand what it means.

Opinion AI guide to read next

8. Learn RAG and AI memory

RAG means Retrieval Augmented Generation.

Simple meaning: the model does not only answer from its training. It retrieves useful information from your files, database, docs, notes, or knowledge base before answering.

This is how AI becomes useful inside a real company.

A model alone does not know your refund policy, bakery process, product list, meeting notes, customer emails, or internal SOPs.

RAG gives AI access to the right knowledge.

Best free resources

This is one of the most practical AI skills.

If you understand RAG, you understand why business AI needs knowledge systems, not only better prompts.

Opinion AI guide to read next

9. Learn machine learning and AI engineering

You can use AI without becoming a machine learning engineer.

But if you want deeper understanding, this layer matters.

Machine learning teaches you the older foundation.

AI engineering teaches you how to build systems that actually work.

Testing. Evaluation. Deployment. Monitoring. Cost. Safety. Data. Feedback loops.

This is where serious builders should spend time.

Best free resources

Pick one serious path.

Finish it slowly.

Do not jump between ten tabs.

Opinion AI guide to read next

The simple path I would follow

Here is the clean order.

Week 1: Elements of AI
Week 2: Google AI Training or IBM SkillsBuild
Week 3: Hugging Face LLM Course
Week 4: OpenAI and Anthropic prompting guides
Week 5: Hugging Face Agents Course
Week 6: Microsoft AI Agents for Beginners
Week 7: n8n or Make Academy
Week 8: Pinecone or LlamaIndex RAG guide
Week 9: Anthropic Academy and Claude Code docs
Week 10: MCP documentation
Week 11: Google Machine Learning Crash Course
Week 12: Made With ML or Full Stack Deep Learning

That is enough to build a serious foundation.

Not a fake AI guru foundation.

A useful one.

My honest take

Free AI education is already good enough to start.

The issue is not access.

The issue is order.

If you only save links, nothing changes.

If you follow a path, build small things, and keep connecting the pieces, your understanding compounds.

Start with basics.

Then LLMs.

Then prompts.

Then agents.

Then workflows.

Then RAG and MCP.

Then production thinking.

And when you want the cleaned, simplified, practical version of these topics, read the Opinion AI guides I added under each section.

That is the whole purpose of this newsletter.