The Best Way to Learn AI, According to Reddit
Ask on r/learnmachinelearning and the same answer surfaces every time: pick one structured course, finish it, and start building something small before you feel ready. The disagreements are about which course. The agreement is that most people fail by collecting resources instead of completing one.
Below is that advice turned into six steps you can actually run, plus the parts of it that are wrong for beginners.
What Reddit agrees on (and what it argues about)
The recurring consensus across r/learnmachinelearning, r/MachineLearning, and r/datascience threads:
- One course, finished, beats five courses started. "Tutorial hell" is the term the subreddits use for the failure mode.
- Build before you're ready. A bad working project teaches more than a perfect notebook you copied line by line.
- Math comes when you hit a wall, not before. The people who start with three months of linear algebra usually quit in month two.
The genuine arguments: top-down versus bottom-up, whether to learn PyTorch or TensorFlow, and whether classical machine learning still matters when LLM APIs handle so much. Those are real trade-offs, not settled questions.
Step 1: Write down one thing you want to build
Not "learn AI." Something with a shape: a classifier that sorts your photos, a chatbot over your own documents, a model that predicts something in a dataset you care about.
This is the step people skip, and it's why the other steps fail. Without a target, every course looks equally relevant and you pick by popularity instead of fit. Someone who wants to fine-tune language models and someone who wants to do tabular forecasting should not take the same first course.
Step 2: Pick one path and commit for eight weeks
| Path | Best if you | Watch out for |
|---|---|---|
| fast.ai, Practical Deep Learning for Coders | Already code, want results in week one | Uses its own library early, so you learn fastai before you learn PyTorch |
| DeepLearning.AI Machine Learning Specialization (Andrew Ng) | Want structure and a gentle math ramp | Slower to a working project; can feel abstract |
| Andrej Karpathy, Neural Networks: Zero to Hero | Want to understand transformers from first principles | Assumes comfort with Python and calculus notation |
| StatQuest and 3Blue1Brown videos | Need visual intuition for the underlying math | Explanation, not practice; pairs with a course, doesn't replace one |
| Kaggle Learn plus a Playground competition | Learn best from real data and a leaderboard | Easy to plateau on tricks that don't generalize |
Two of these are complements, not paths: 3Blue1Brown's neural network series and StatQuest sit alongside whatever you chose. Everything else, pick one and stop shopping.
Step 3: Build the ugly version in week two
The single highest-leverage deviation from most course paths: start your own project long before the course ends. Use whatever you know at that point, even if it's a scikit-learn baseline that barely works.
Building early does two things. It exposes the gaps that passive watching hides, and it gives every later lecture a place to land. When the course covers regularization, you will already have an overfitting problem waiting for it.
Step 4: Test yourself instead of rewatching
This is where most self-taught learners lose months. Rewatching a lecture feels productive because the material feels familiar the second time. Familiarity is not recall.
In Roediger and Karpicke's 2006 experiments, students who studied a passage once and then tested themselves repeatedly outperformed students who restudied the same passage repeatedly, on delayed tests a week later. The restudy group actually predicted they would do better. They were wrong. Dunlosky and colleagues' 2013 review of learning techniques reached a similar conclusion: practice testing and distributed practice earned high utility ratings, while rereading and highlighting did not.
Practically, after each module:
- Close the video and write down, from memory, what the algorithm does and when it fails.
- Turn your own notes into questions rather than summaries. If you keep course notes in a notebook or on your phone, Qora generates multiple choice and open recall questions from your own material, which is closer to the retrieval effect than rereading is. The same approach applied to any dense source works whether the material is a lecture or a paper.
- Explain one concept out loud as if to someone who has never heard of it. The gaps become audible.
If you prefer flashcards, converting your notes into Anki cards gets you spaced repetition for the vocabulary layer: loss functions, optimizers, evaluation metrics. Anki is genuinely excellent at terminology and weak at conceptual reasoning, so use it for the former.
Step 5: Add math when it blocks you, not before
You do not need to finish a linear algebra textbook to train a model. You do need to understand a gradient before you can debug a training run that diverges.
The efficient order is reactive. Hit a wall, then learn the specific thing. Backpropagation confusing? That's the chain rule. Model won't converge? That's gradients and learning rates. Confused by why your accuracy is misleading? That's conditional probability and base rates. Each of these is a few hours of targeted study, not a semester.
Step 6: Put your work somewhere it can be criticized
Push the project to GitHub with a README that explains what you tried and what failed. Post it in r/learnmachinelearning and ask a specific question. Vague posts ("how do I get better?") get vague replies. Specific ones ("my validation loss is lower than training loss, here's the code") get real answers.
This step is uncomfortable and it is the one that compresses the timeline most.
Where Reddit's advice breaks down
Three honest caveats.
Survivorship bias is heavy. The people posting "I self-taught in six months and got hired" are visible. The far larger group who stalled at month three is not posting. Treat timeline claims as a best case, not a median.
"Just build stuff" fails without a floor. It's good advice for someone who can already write Python and read an error message. For someone who cannot, it produces a copied notebook they cannot modify. If you can't yet write a loop and a function from scratch, spend two weeks on Python first.
The subreddits have different audiences. r/MachineLearning skews toward research and PhD-track discussion, where advice like "read papers daily" is correct for that audience and mostly wrong for a career switcher. r/learnmachinelearning and r/datascience are closer to the applied path. Check which room you're standing in before taking the advice.
There's also a 2026-specific trap: LLM assistants will write the model code for you. That's fine for shipping and terrible for learning, because you get a working result with no encoded understanding. Use them to explain errors and review your code, not to produce the first draft of anything you're trying to learn.
Common questions
How long does it take to learn AI?
For a working project you can explain end to end, most self-taught learners report somewhere between three and nine months of consistent part-time study. The variable that matters most is not talent but whether you complete one path instead of restarting three times. Prior programming experience roughly halves it.
Do I need to be good at math to learn AI?
You need high school algebra to start and comfort with derivatives, matrices, and basic probability to go deep. You do not need those before day one. The reactive approach in Step 5 works better for most people than front-loading a full math curriculum, which is where a lot of learners quit.
Is a degree required to work in AI?
Not for most applied engineering roles, where a portfolio of real projects and demonstrated ability in interviews carry the weight. It is effectively required for research positions, which are where most published papers and most competitive job postings come from. Be clear which of the two you're aiming at, because the study paths diverge sharply.
Should I learn machine learning or just learn to use LLM APIs?
Depends on your Step 1 answer. If you want to build products on top of models, start with API work and prompt design, since you'll ship faster and the fundamentals can follow. If you want to train, fine-tune, evaluate, or debug models, you need the machine learning foundation, and the API-first route will stall you at the point where things break.
The short version
The best way to learn AI, going by what actually works in the threads, is unglamorous: choose one target project, commit to one course for eight weeks, start building in week two, and replace rewatching with self-testing. The resource you pick matters far less than whether you finish it.
Start today by writing one sentence describing what you want to build, then pick the single row from the table above that matches it. Close every other tab.