Udemy
Machine Learning A-Z (Udemy) Review — Honest Analysis of 44 Learner Opinions
"Machine Learning A-Z" is the course people point to when they want a broad, hands-on tour of classical machine learning without heavy math. Across roughly 44 hours in Python and R, Kirill Eremenko and Hadelin de Ponteves walk through the main algorithm families using a template-based coding style that reviewers find accessible and practical, backed by an unusually active Q&A community. The main caveats: math theory stays shallow, some sections (NLP, deployment) are thin, and it is a strong first step rather than a course that alone makes you job-ready.
Final score
from 44 analysed opinions
Published AI-researched, editor-audited
Distribution of opinions
Per-criterion scores
Around 44 hours covering regression, classification, clustering, association rule learning, reinforcement learning, NLP, and deep learning, in both Python and R. Reviewers call it comprehensive and well paced; the main gap is that NLP only reaches bag-of-words and math theory stays light.
Kirill Eremenko and Hadelin de Ponteves are the most-praised element — reviewers say they make a complicated topic accessible to a wide audience and break complex concepts into digestible lessons, with Hadelin's step-by-step coding singled out repeatedly.
A one-time Udemy purchase that frequently goes on deep discount, with ~44 hours and lifetime access. With roughly 800K enrolments and a 4.5 average, reviewers consistently say it is worth it even at full price for the breadth you get.
No live mentorship or graded project feedback, but reviewers highlight an unusually active Q&A community — "dozens of questions being filed every day" — as where the course really shines for getting unstuck.
Template-based, hands-on coding on real datasets builds working intuition, but it is an on-ramp rather than a job guarantee. Deployment/production is barely covered and it "won't make you an AI guru" — a strong first step, not a finishing course.
What learners said
What people loved
6- Instructors make a complicated topic accessible — reviewers say they break complex concepts into digestible, well-paced lessons×19
- Genuinely hands-on: step-by-step template-based coding on real datasets in both Python and R×15
- Very broad coverage (~44 hours) across regression, classification, clustering, reinforcement learning, NLP and deep learning×13
- Unusually active Q&A community with dozens of questions answered every day — where the course "really shines"×8
- Strong value for a one-time Udemy purchase with lifetime access; reviewers say it is worth it even at full price×11
- Teaching both Python and R lets learners highlight dual-language skill on a CV×5
What frustrated learners
5- Math theory stays shallow — the "why" behind algorithms is not explained in deep detail×9
- Won't make you an AI or data-science guru on its own — it's a first step, not a finishing course×7
- Code examples are template-heavy and can be thin on structure, modularity, and current library versions×6
- Some sections are shallow: NLP only reaches bag-of-words, and deployment/production is barely covered×5
- No live mentorship, graded project feedback, or job guarantee×4
Real quotes from real users
“Udemy's Machine Learning A-Z course. All lessons in R and Python. Wide range of content.”
“The instructors, Kirill Eremenko and Hadelin de Ponteves, have done a great job to make machine learning, a fairly complicated topic, accessible to a wide audience.”
“Where Machine Learning A-Z really shines is its support and community features. This is a very active community with dozens of questions being filed every day.”
“Machine Learning A-Z probably won't make you an AI and data science guru, and the coding examples left a bit to be desired when it comes to structure and modularity.”
“It is one of the best Machine Learning courses you can get at an affordable price; it's comprehensive (44 hours of content) yet engaging, it's hands-on yet detailed.”
“It is an amazing course that can take people with very little knowledge to being very comfortable implementing and building real Machine Learning applications.”
“I particularly appreciated the real-world datasets and the step-by-step coding approach.”
“Mathematical theory isn't explained in deep detail, and there's limited discussion of deployment and production concerns.”
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How we evaluated this
This review synthesizes 44 opinions collected across the public web. Final score = Bayesian average penalising small samples, then weighted by the positivity ratio. No paid placements, no hidden agenda.
- 2 from Hacker News
- 18 from Blogs
- 18 from Official course platform
- 6 from Other