Stephen Klein recently shared something that caught my attention: "MIT AI courses are FREE right now (No coding required)." He added a practical warning that felt like a real PSA: you might want to share it because platforms often downrank posts that send people off-site.

His bigger point landed even harder: many people are paying for "AI certifications" while MIT is giving real AI learning away for free. If you are trying to skill up in 2026, that contrast matters.

In this post, I want to expand on Klein's list, add context for who each course is best for, and give you a simple plan to turn free MIT material into a coherent learning path. Because the real challenge is not finding resources. It is choosing the right sequence, building projects that prove competence, and avoiding credential traps.

The real takeaway: stop paying for confusing signals

Klein is not just sharing links. He is calling out a pattern:

People chase shiny certificates when they really need durable understanding.

AI hiring signals are messy. Some recruiters look for degrees, others look for portfolios, and many are still figuring out what "GenAI experience" even means. In that environment, expensive certifications can feel comforting. But they often teach surface-level tooling, not fundamentals.

MIT's free courses are valuable because they help you build transferable mental models: how learning algorithms work, why deep learning behaves the way it does, what foundation models changed, and where algorithms and data structures still matter.

9 free MIT AI courses Klein recommends, with context

Below is Klein's list, with a quick "why take it" lens so you can choose intentionally.

1) Introduction to Machine Learning

Klein highlights that it covers "supervised + reinforcement learning (images + sequences)." This is a strong starting point if you want the standard ML toolkit: framing problems, training models, and understanding evaluation.

Link: https://lnkd.in/d6WyUQsx

2) Artificial Intelligence

This is the classic "survey" course: knowledge representation, problem solving, vision, and language. If you want breadth before depth, this is your map of the territory.

Link: https://lnkd.in/dMxm7qW2

3) Foundation Models and Generative AI

Klein calls out that it explains "why foundation models changed AI" and how self-supervised learning made it possible, with a non-technical orientation. This is ideal for product leaders, operators, educators, and anyone who needs conceptual clarity without living in code.

Link: https://lnkd.in/d5-Dqfp4

4) AI 101

A beginner-friendly workshop plus a hands-on exercise to train your own algorithm. If you are intimidated by the term "machine learning," start here and build confidence fast.

Link: https://lnkd.in/dZ8VRsB6

5) Introduction to Deep Learning

A focused bootcamp-style course that moves quickly into neural networks with applications in NLP, vision, biology, LLMs, and GenAI. Take this when you are ready to understand why deep learning dominates so many tasks.

Link: https://lnkd.in/dX7ESNqx

6) ML with Python (MITx)

This is for learners who want reps. Klein notes hands-on Python projects spanning linear models through deep learning and reinforcement learning. If you learn by doing, prioritize this.

Link: https://lnkd.in/dj228C79

7) How to AI (Almost) Anything

I like this inclusion because it pushes beyond text. Klein mentions images, audio, sensors, music, art, and real-world multimodal systems. It is a reminder that "AI" is bigger than chat.

Link: https://lnkd.in/dhC4j6WU

8) Artificial Intelligence in K-12 Education

This one stands out as mission-driven and practical: GenAI fundamentals plus project-based learning for classroom use cases. Even if you are not a teacher, it is a great example of applied AI literacy.

Link: https://lnkd.in/dRZcXuzk

9) Introduction to Algorithms

Klein frames algorithms and data structures as the base of "fast and reliable AI systems." This is the unsexy multiplier. Better algorithmic thinking makes you a stronger engineer, analyst, and model user.

Link: https://lnkd.in/ddQqcDTh

A simple way to choose your path (without getting overwhelmed)

When people see a list like this, they often try to do everything. That is how good intentions die.

Here is a practical decision tree.

If you are a true beginner

Start with:

AI 101
Artificial Intelligence (for breadth)
Introduction to Machine Learning

Goal: learn the language of the field and the basic workflow: data, features, training, evaluation, iteration.

If you want to build GenAI products or workflows

Start with:

Foundation Models and Generative AI
Introduction to Deep Learning
ML with Python (MITx)

Goal: understand what foundation models are good at, where they fail, and how to evaluate them with more than vibes.

If you want stronger engineering fundamentals

Start with:

Introduction to Algorithms
Introduction to Machine Learning
Introduction to Deep Learning

Goal: become the person who can ship systems that are correct, efficient, and maintainable, not just impressive demos.

Turning free courses into career signal

Klein's frustration with paid "AI certifications" is really about signal quality. Courses are input. Evidence of skill is output.

Here is how to create output while you learn.

Build 3 small portfolio projects (one per layer)

Classic ML project

Pick a tabular dataset (churn, pricing, fraud, health)
Train a baseline model
Explain metrics and tradeoffs in plain language

Deep learning project

Fine-tune or train a simple neural net
Include an error analysis section
Show what you tried when performance stalled

GenAI system project

Build a retrieval-augmented workflow
Evaluate with a small test set you wrote
Track failures like hallucinations and prompt injection attempts

Write one "learning memo" per course

A short post beats a badge. After each course, publish a memo answering:

What did I believe before?
What do I believe now?
What would I warn a beginner about?

This is also where Klein's point about distribution matters. Social platforms may not love outbound links, but they do reward original insight. Summaries, diagrams, and lessons learned travel well.

Why "no coding required" still matters

Klein leads with "No coding required" for a reason: AI literacy is now a baseline skill, not a specialist hobby.

Non-technical learners can and should understand:

The difference between training and inference
Why data quality beats model complexity more often than people admit
What overfitting looks like in real life (not just in charts)
Why evaluation is the hardest part of GenAI adoption

And technical learners benefit too: if you cannot explain your system clearly, you probably do not understand it well enough to trust it.

A quick note on motivation (and the Beaver Ring aside)

Klein ended with a funny personal note about MIT culture, saying that even though he went to Harvard he was "always pissed off" he did not go to MIT because the Beaver Ring is "too cool for school." It is a good reminder that learning is emotional. Pride, curiosity, and community matter.

If a free MIT course list gives you the nudge to start, use it. You do not need permission, and you definitely do not need an overpriced certificate to begin.

Next steps: pick one course and start this week

If you do nothing else:

Choose one course from Klein's list that matches your goal
Block two 60-minute sessions on your calendar
Take notes in a format you can publish later

Momentum beats perfection.

This blog post expands on a viral LinkedIn post by Stephen Klein, Founder & CEO, Curiouser.AI | Berkeley Instructor | Building Values-Based, Human-Centered AI | LinkedIn Top Voice in AI. View the original LinkedIn post →