How to Learn AI in 2026: What Reddit Actually Recommends

The r/learnmachinelearning community has produced a fairly stable consensus over years of discussions: start with Andrew Ng's Machine Learning Specialization on Coursera. It appears in almost every "where do I start" thread in the subreddit, and the recommendation holds up across skill levels. The course is free to audit, which removes the cost barrier, and it covers the mathematical and conceptual foundations that the technical subreddits respect.

Beyond Andrew Ng, the community splits based on what you actually want to do. r/learnmachinelearning (beginner-friendly, 171K members) recommends starting with Python basics via YouTube tutorials like Corey Schafer's channel, then moving to NumPy and Pandas before hitting ML frameworks. r/MachineLearning (research-focused) recommends a full math stack first: linear algebra, calculus, probability theory, and statistics before touching any code.

fast.ai gets consistent praise across both communities as the most practical alternative. The courses are free, project-first, and updated more frequently than most university-adjacent offerings. A frequently cited r/learnmachinelearning comment: "fast.ai assumes you're not a researcher and teaches you to ship things, which is what most people actually need."

For non-technical learners who do not plan to write ML code, the community's advice shifts entirely. r/artificial and r/productivity discussions point toward AI fundamentals courses that cover how to use AI tools, prompt engineering, and generative AI workflows. This is a different goal from ML engineering and needs a different course.

Reddit discussions in 2026 have increasingly divided AI learning into three distinct tracks that require different resources and timelines. Picking the wrong track wastes months.

AI Engineering (fastest growing path)

Building applications with existing AI models: LLM APIs, retrieval-augmented generation, AI agents, prompt engineering. This track is wide open in 2026 because it requires software development skills rather than ML research knowledge. Timeline: 3-6 months from software engineering background. Key resources: Hugging Face courses, LangChain documentation, OpenAI API docs. r/artificial and r/webdev cover this most.

ML Engineering / Data Science

Training, fine-tuning, and deploying machine learning models. Requires Python proficiency, statistics, and hands-on experience with frameworks like PyTorch. Timeline: 12-18 months from beginner. This is what r/learnmachinelearning primarily discusses. Andrew Ng's Specialization and fast.ai are the most cited starting points.

AI Business / Non-Technical

Understanding AI well enough to make decisions, manage AI projects, or use AI tools effectively in a professional context. No coding required. Timeline: 1-3 months. This track gets the least Reddit discussion because the ML-focused communities dominate, but it is the most common actual use case for working professionals. Coursera and Udemy AI fundamentals courses serve this path.

The community warning from r/datascience: "Most people asking 'how do I learn AI' actually want AI Engineering, not ML Engineering. They're different fields with different requirements, and picking the wrong track wastes a year."

Reddit is straightforward about free AI learning: the quality is high and certificates have marginal impact on hiring decisions. One r/learnmachinelearning thread put it directly: "You don't need to pay for anything. Everything valuable is free if you know where to look."

Andrew Ng's Machine Learning Specialization (Coursera - free to audit)

The community's consensus starting point for ML. Three courses covering ML fundamentals, advanced algorithms, and unsupervised learning. Free to audit means you get the content without the certificate. Reddit verdict: "Still the best structured introduction to ML, years later."

fast.ai Practical Deep Learning

Jeremy Howard's free course takes a top-down, project-first approach that produces working models faster than theory-first alternatives. r/learnmachinelearning calls it "the most practical option for people who want to ship things." No cost, constantly updated.

Kaggle Learn

Free micro-courses covering Python, ML, deep learning, and more. Competitions give you real projects to show in a portfolio. Reddit treats Kaggle as the closest thing to a practical credential: "A top 10% finish in a Kaggle competition carries more hiring weight than any certificate."

Hugging Face Courses

Free courses on transformers, NLP, diffusion models, and LLM fine-tuning. The go-to for 2026 LLM skills. r/MachineLearning treats Hugging Face documentation and tutorials as required reading for anyone working with modern models.

3Blue1Brown (YouTube)

Visual explanations of linear algebra and calculus that multiple subreddits cite as the best math foundation for ML. Free, no registration. Reddit recommendation: "Watch the Essence of Linear Algebra series before touching any ML course."

Reddit's bias toward free resources is well-founded for content quality. The debate is about structure, accountability, and what you actually get from a paid course that you cannot get free.

The main case for paid courses comes down to three factors. First: structure. Free resources require you to build your own curriculum, decide your own sequence, and keep yourself on track. Paid courses give you a predetermined path that eliminates decision fatigue about what to learn next. One r/learnmachinelearning user framed it: "The course content is the same. What you're paying for is someone else having figured out the sequence."

Second: completion rates. Free Coursera audits have significantly lower completion rates than paid versions, and the ML community acknowledges this. If you know you need external accountability to finish a course, that accountability has real value.

Third: documentation. A completed paid course gives you a certificate you can attach to a LinkedIn profile or resume. This matters most for non-technical roles where AI knowledge is an add-on skill, not a primary hiring criteria. r/datascience is clear that certificates do not substitute for portfolio projects in technical hiring, but for business and management roles, documented training does carry weight.

Udemy's specific advantage over Coursera for this purpose: one-time payment, lifetime access. Coursera's monthly subscription model means you pay for every month you take to complete a course. For courses you want to revisit as AI tools evolve, ownership beats rental.

The most consistent piece of advice across r/learnmachinelearning, r/datascience, and r/artificial: every timeline estimate you find online is optimistic.

The community's actual documented experiences break down by track. For AI Engineering (building applications with AI APIs and LLMs), a software engineer with existing Python skills reported moving from zero LLM knowledge to a deployed AI application in about three months at 10 hours per week. The lower barrier is real: you are orchestrating existing models, not training them.

For ML Engineering, the documented range is 12-18 months of consistent study before landing a first role. A Zero to Mastery bootcamp student in r/WGU_CompSci used the Complete Machine Learning and Data Science course for a capstone project in an 11-month program and landed a software engineer offer with ML components. That is an accelerated case with structured curriculum and a degree program behind it.

For complete beginners with no coding background, the community consensus is 18-24 months to ML Engineer readiness. Python alone takes 2-3 months to reach ML-useful fluency.

The pattern from r/learnmachinelearning discussions: people who build projects throughout their learning consistently land roles faster than people who complete more courses first. "Project-first learners outperform course-collectors at every timeline" was one r/learnmachinelearning comment with significant upvotes.