CHands-on reviewedEditor's pick
Coursera
Machine Learning Specialization (Andrew Ng)
For: Engineers without classical ML background entering AI work
4.9(33K)
Beginner · ~3 months (5-10h/wk) · Free to audit · $49 cert
Machine Learning Specialization (Andrew Ng)vsNeural Networks: Zero to Hero (Andrej Karpathy)
Side-by-side comparison on level, duration, pricing, instructor, tier. Editor verdict on which course wins for which buyer.
C
Coursera
Machine Learning Specialization (Andrew Ng)
The successor to Andrew Ng's original 2011 ML course — the single most-watched ML course in history (4M+ students). Three courses cover supervised, unsupervised, and reinforcement learning + neural networks from first principles. In 2026, this is still the foundational ML curriculum every serious AI engineer is expected to know. Take this before any LLM-internals course if you don't have classical ML background.
▶
YouTube
Neural Networks: Zero to Hero (Andrej Karpathy)
The single most-recommended free resource in modern AI education. Karpathy builds a working autograd engine, then a character-level language model, then a tokenizer, then a working GPT — all from scratch in PyTorch, explaining every line. The 'Let's build GPT' and 'Let's build the GPT Tokenizer' lectures specifically have become canonical references — every senior AI engineer has watched them. The catch: it's 25 hours of dense math-and-code video. You won't follow if you can't keep up with Python + linear algebra. But if you can, no paid course delivers comparable depth.
Side-by-side
| Dimension | Machine Learning Specialization (Andrew Ng) | Neural Networks: Zero to Hero (Andrej Karpathy) |
|---|---|---|
| Provider | Coursera | YouTube |
| Editorial tier | Hands-on reviewed | Hands-on reviewed |
| Level | Beginner | Advanced |
| Format | self paced | video |
| Duration | ~3 months (5-10h/wk) | ~25 hours (11 lectures) |
| Pricing | Free to audit · $49 cert | Free |
| Instructor | Andrew Ng — Founder DeepLearning.AI; co-founder Coursera; founding lead Google Brain | Andrej Karpathy — Co-founder OpenAI; former Director of AI at Tesla |
| Rating | ★ 4.9 (33,420 on Coursera) | No public rating |
| Topics | llm fundamentals, fine tuning | llm fundamentals, fine tuning, ai engineering |
| Last verified | 2026-05-24 | 2026-05-23 |
Pros & cons
Machine Learning Specialization (Andrew Ng)
Pros
- +Andrew Ng — most-authoritative ML educator alive
- +First-principles foundation that compounds across every other AI course
- +Audit free; cert optional
- +Modernized for Python + scikit-learn (the 2011 original was Octave)
Cons
- −~100 hours of commitment — months of work
- −Pre-LLM era ML — supplement with a separate LLM course for 2026 relevance
Neural Networks: Zero to Hero (Andrej Karpathy)
Pros
- +By a co-founder of OpenAI — no higher-authority instructor exists
- +Free, on YouTube, with companion GitHub code
- +The "Let's build GPT from scratch" lecture is the single most-cited free resource in AI education
Cons
- −25-hour commitment of dense material — you have to actually do the exercises
- −Pre-reqs are real: comfortable Python + undergrad linear algebra
- −No certificate, no community — pure self-direction
Which course is for whom?
Machine Learning Specialization (Andrew Ng)
Best for
- · Engineers without classical ML background entering AI work
- · Anyone wanting the canonical foundation
Not ideal for
- · Engineers focused only on applied LLM work — RAG/agents courses are higher ROI
Neural Networks: Zero to Hero (Andrej Karpathy)
Best for
- · Engineers who plan to train, fine-tune, or research LLMs at depth
- · Anyone tired of "wrapper" courses who wants to understand transformers from first principles
Not ideal for
- · Casual learners — the dropout rate on the lecture series is high for a reason
- · Anyone wanting application patterns (LangChain, prompt engineering) — wrong course
Editor's short verdict
Take Machine Learning Specialization (Andrew Ng) first if you're new to the topic; once you have the basics, Neural Networks: Zero to Hero (Andrej Karpathy) is the natural next step. They're complementary in a learning path, not directly competing.
▶Hands-on reviewedEditor's pick
YouTube
Neural Networks: Zero to Hero (Andrej Karpathy)
For: Engineers who plan to train, fine-tune, or research LLMs at depth
Advanced · ~25 hours (11 lectures) · Free
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