Last updated: 1 November 2026. Written by Josh Hutcheson, OnlineCourseing editor. Every rating, review count, learner count, course length and last-updated date below was read from each course’s own Udemy page in a logged-out browser on 1 November 2026. See our review methodology.
QUICK VERDICT
Bottom line: The best Udemy machine learning course for most people is Machine Learning A-Z (4.5 from 206,721 ratings, 1.2 million learners, updated June 2026), the most-reviewed and most complete tour of classical ML, taught in both Python and R. If you want a current, project-driven path in Python only, take the Complete A.I. & Machine Learning, Data Science Bootcamp (4.7, updated February 2026). Either one costs about the price of lunch in a Udemy sale.
Best overall: Machine Learning A-Z (Kirill Eremenko, Hadelin de Ponteves)
Best current project path: Complete A.I. & Machine Learning, Data Science Bootcamp (Andrei Neagoie, Daniel Bourke)
Best for practitioners: Feature Engineering for Machine Learning (Soledad Galli)
Pricing: the new-learner offer on 1 November 2026 read “courses from $14.99”; every pick is also in the Personal Plan (from $13.00 a month)
Skip if: you want ChatGPT, LLM and agent skills; that is our best Udemy AI courses list, not this one
See Machine Learning A-Z on Udemy →
Best Udemy Machine Learning Courses in 2026: 7 Picks Worth Your Money (Honest Rankings)
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Search “machine learning” on Udemy and you get thousands of results. Most are competent; a handful are excellent, taught by instructors with six-figure learner counts and ratings that have held at 4.5 or better across tens of thousands of reviews. This guide separates the second group from the first, and it stays on traditional machine learning: the algorithms, the statistics underneath them, scikit-learn, TensorFlow and Keras, supervised and unsupervised methods. If you want ChatGPT, prompt engineering and building with large language models instead, that is a different skill set with a different list: see our best Udemy AI courses.
We picked these seven on three measures. First, a rating of 4.5 or higher on the day we checked. Second, a large review count, because a 4.7 from 300 people tells you far less than a 4.5 from 200,000. Third, a distinct use case, so the list covers absolute beginners, Python-first coders, R users, web developers and people already working with real data. We re-read every figure on 1 November 2026, and the changes since our May check are on the page rather than hidden: two courses lost a tenth of a point (the Python bootcamp from 4.6 to 4.5, the R bootcamp from 4.7 to 4.6), the TensorFlow course slipped from 4.7 to 4.6, and the Zero To Mastery bootcamp rose from 4.6 to 4.7. All seven are still live and enrolling.
We also say plainly where a course is old. Three of the seven were last updated before 2023. For classical machine learning that matters less than it would for an AI tools course, because regression, decision trees and clustering have not changed, but library versions and screenshots drift, and we flag it on each pick. We earn a commission if you enroll through our links; the order is set by the courses, not the payout, and every link on this page pays the same Udemy rate.
WHAT LEARNERS SAY
Learners on r/learnmachinelearning keep naming the same two courses when someone asks for a Udemy starting point: Machine Learning A-Z for classical methods and its sister course Deep Learning A-Z for neural networks. The praise is for the visual explanations; the recurring complaint is that the code templates do the thinking for you. Both points match what we found, and they shape the picks below.
1. Machine Learning A-Z: AI, Python & R — Best Overall
Kirill Eremenko, Hadelin de Ponteves (SuperDataScience, Ligency) · Rating 4.5 (206,721 ratings) · 1,228,857 learners · Updated June 2026 · 49h 14m, 474 lectures · All levels
If you want one course that covers classical machine learning end to end, this is it. Its current title is “Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R”, and with more than 206,000 ratings and 1.2 million learners it is by a wide margin the most-tested ML course on Udemy. It walks through regression, classification, clustering, association-rule learning, reinforcement learning, natural language processing, dimensionality reduction and model selection, and every technique is taught in both Python and R, so you can follow along in whichever language you use.
The June 2026 update is the reason it still leads. The course now reaches past classical ML into deep learning and model deployment on AWS, which turns it from a tour of algorithms into a first look at the whole workflow. Each section follows the same rhythm: an intuition lecture that explains the idea with diagrams, then a practical lecture where you run a ready-made code template on a small dataset.
Who it is for: beginners and career-switchers who want one structured first pass through the whole ML toolkit before deciding where to specialize.
The honest catch: the SuperDataScience style leans on intuition and templates rather than derivation. That gets you moving and seeing results quickly, but it is the main criticism learners raise: you can finish a section without being able to write the model yourself. Pair it with pick 3 or pick 7 if you want to build from a blank notebook.
See Machine Learning A-Z on Udemy →
2. Python for Data Science and Machine Learning Bootcamp — Best Code-First Path
Jose Portilla (Pierian Training) · Rating 4.5 (161,211 ratings) · 837,849 learners · Updated May 2020 · 24h 54m, 165 lectures · All levels
Jose Portilla is one of Udemy’s most-followed data-science instructors, and this is his flagship ML course, with more than 161,000 ratings. It is more code-first than Machine Learning A-Z: you work through NumPy, pandas, Matplotlib, seaborn and scikit-learn, then apply them to linear and logistic regression, k-nearest neighbors, decision trees and random forests, support vector machines, k-means clustering, recommender systems, natural language processing and a short introduction to neural networks. The teaching is clean and patient, which is why it is still a common answer when beginners ask where to start.
Its strength is the data-handling half. Many ML courses rush past pandas to get to the algorithms; this one spends real time on loading, cleaning and plotting data in Jupyter notebooks, which is most of the day-to-day work in a data job.
Who it is for: learners who want to commit to Python and build a real data-science workflow, not just run algorithms.
The honest catch: the last update was May 2020. The scikit-learn concepts are unchanged, but expect library versions, some syntax and the deep-learning section to look dated, and the rating has slipped from 4.6 to 4.5 since our May check. If you want the same ground with a current update, pick 3 covers it.
See the Python ML Bootcamp on Udemy →
3. Complete A.I. & Machine Learning, Data Science Bootcamp — Best Modern Project Path
Andrei Neagoie, Daniel Bourke (Zero To Mastery) · Rating 4.7 (31,155 ratings) · 173,296 learners · Updated February 2026 · 43h 59m, 384 lectures · All levels
The best-maintained broad ML bootcamp on this list and the most project-driven. Andrei Neagoie runs Zero To Mastery, one of Udemy’s largest developer teaching brands, and Daniel Bourke is the machine learning engineer behind the popular free PyTorch materials; together they teach the full data-science workflow in Python (pandas, NumPy, Matplotlib, scikit-learn) and then move into deep learning with TensorFlow, built around real datasets and two end-to-end projects rather than toy examples.
It is also the only pick whose rating went up since May, from 4.6 to 4.7, as the review count passed 31,000. That is the best available signal that the February 2026 update landed well. Zero To Mastery sells the same course inside its own membership, so if you plan to take several of its courses, compare that with buying this one on Udemy.
Who it is for: people who learn by building and want a current, portfolio-oriented path from data wrangling through neural networks.
The honest catch: at 44 hours it is a serious time commitment, and “all levels” is generous; you will move much faster with some Python already in hand. Complete beginners should take a Python course first; our best Udemy Python courses list has the options.
See the ZTM ML Bootcamp on Udemy →
4. Data Science and Machine Learning Bootcamp with R — Best for R Users
Jose Portilla (Pierian Training) · Rating 4.6 (17,884 ratings) · 101,378 learners · Updated December 2020 · 17h 45m, 128 lectures · All levels
The best-reviewed course here that teaches machine learning in R rather than Python. It covers R fundamentals, data frames and visualization with ggplot2 and plotly, and then the core algorithms: linear and logistic regression, k-nearest neighbors, decision trees and random forests, support vector machines, k-means clustering, natural language processing and neural nets. For statisticians, academics and analysts who already live in R, it is the natural pick, because it never asks you to switch tools.
Who it is for: R users, researchers and analysts who want machine learning without moving to Python.
The honest catch: its last update was December 2020. R’s core ML packages are stable, so the material holds up, but the rating has eased from 4.7 to 4.6, and if you have no language preference Python is the more in-demand industry skill.
See the R ML Bootcamp on Udemy →
RECOMMENDED PARTNER — UDEMY
One purchase, lifetime access
Every course here is a one-time buy with lifetime access, including future updates from the instructor. Udemy’s new-learner offer read “courses from $14.99” on 1 November 2026, and eligible purchases carry a 30-day refund window under Udemy’s refund policy. All seven picks are also in the Personal Plan subscription, which does not carry that refund window.
Affiliate partnership: we may earn a commission when you enroll via our links. It does not change the order.
5. Complete TensorFlow 2 and Keras Deep Learning Bootcamp — Best for Deep Learning
Jose Portilla (Pierian Training) · Rating 4.6 (8,948 ratings) · 56,529 learners · Updated June 2022 · 19h 12m, 116 lectures · All levels
Once you have the classical foundations, this is the natural next step into neural networks. It teaches TensorFlow 2 and the Keras API: artificial, convolutional and recurrent neural networks, natural language processing with RNNs, autoencoders, generative adversarial networks and deploying a model, all at the same methodical pace Portilla is known for. It suits learners who found pick 2 easy to follow and want the same voice for the deep-learning half.
Who it is for: learners who already know basic Python and ML and want a focused, hands-on path into deep learning with Keras.
The honest catch: it is not an entry point, and its June 2022 update means some TensorFlow and Keras details have moved on. If you would rather learn deep learning in PyTorch, which most current research code uses, see the PyTorch bootcamp in our Udemy AI courses list or the cross-platform deep learning courses guide.
See the TensorFlow 2 Bootcamp on Udemy →
6. Machine Learning with JavaScript — Best for Web Developers
Stephen Grider · Rating 4.7 (3,521 ratings) · 33,132 learners · Updated February 2026 · 17h 40m, 186 lectures · All levels
An unusual and useful pick: machine learning taught in JavaScript, by Stephen Grider, one of Udemy’s best-known web-development instructors. Instead of importing a library and calling fit, you implement the algorithms yourself, k-nearest neighbors, linear regression with gradient descent, logistic and multinomial logistic regression, and a first look at TensorFlow.js. Writing the math by hand is what makes the mechanics click, and it is the same reason first-principles courses are recommended to people who found the template style of pick 1 too easy.
Who it is for: JavaScript and front-end developers who want to understand ML without leaving their language, and anyone who learns best by building algorithms from scratch.
The honest catch: JavaScript is not the industry language for ML; Python is. Treat this as a way to understand machine learning deeply, not the most direct route to an ML job. The small review count reflects a niche audience rather than weak teaching.
See ML with JavaScript on Udemy →
7. Feature Engineering for Machine Learning — Best for Practitioners
Soledad Galli (Train in Data) · Rating 4.5 (3,834 ratings) · 28,056 learners · Updated March 2025 · 13h 23m, 202 lectures · Intermediate in practice
Most ML courses rush the part that decides how well a model does on real data: preparing the variables. This course fills that gap. Soledad Galli, who maintains the open-source Feature-engine Python library, covers missing-data imputation, encoding categorical variables, discretization, outlier handling, variable transformation, feature scaling and creating new features from dates and text, with the code to do each one in pandas, scikit-learn and Feature-engine.
Who it is for: anyone who has finished an introductory ML course and now works with messy, real-world data.
The honest catch: it is narrow by design. It assumes you can already train a model and will not teach the algorithms. Take it as the practical second course after picks 1 to 3, not as a starting point.
See Feature Engineering on Udemy →
Udemy Machine Learning Courses Compared at a Glance
| Course | Rating (ratings) | Learners | Length | Updated | Best for |
|---|---|---|---|---|---|
| Machine Learning A-Z | 4.5 (206,721) | 1,228,857 | 49h 14m | Jun 2026 | Broad first pass, Python and R |
| Python for Data Science & ML Bootcamp | 4.5 (161,211) | 837,849 | 24h 54m | May 2020 | Code-first Python workflow |
| Complete A.I. & ML, Data Science (ZTM) | 4.7 (31,155) | 173,296 | 43h 59m | Feb 2026 | Current, project-driven path |
| Data Science & ML Bootcamp with R | 4.6 (17,884) | 101,378 | 17h 45m | Dec 2020 | R users |
| Complete TensorFlow 2 & Keras | 4.6 (8,948) | 56,529 | 19h 12m | Jun 2022 | Deep learning with Keras |
| ML with JavaScript | 4.7 (3,521) | 33,132 | 17h 40m | Feb 2026 | Web developers, from scratch |
| Feature Engineering for ML | 4.5 (3,834) | 28,056 | 13h 23m | Mar 2025 | Practitioners with real data |
Ratings, review counts, learner counts, lengths and update dates read from each course page on 1 November 2026. They change daily; treat them as a snapshot and check the live listing before buying.
How to Vet Any Udemy Machine Learning Course Before You Buy
Udemy is an open marketplace: anyone can publish, so quality varies enormously. A few quick checks reliably separate strong courses from filler. Run them on anything you consider, on this list or not.
Check the rating and the number of ratings
A 4.9 from 12 reviews tells you almost nothing; a 4.5 from 50,000 is a real signal. Treat the review count as the confidence behind the average and look for a few thousand ratings before trusting the score. Be wary of new courses carrying a “Highest Rated” badge off a small sample. The picks here run from about 3,500 ratings to more than 206,000.
Check the last-updated date, and weigh it against the topic
Look for “Last updated” under the course title. It matters less for classical ML than for generative AI: regression, decision trees and clustering have been stable for years, so a 2020 course on the fundamentals is still mostly accurate. What dates is the tooling around it, library versions, APIs and screenshots. Three picks here were last updated before 2023, and we flag each one.
Check the instructor’s track record
Click the instructor’s name to see total learners, course count and other reviews. Everyone on this list, SuperDataScience, Jose Portilla, Andrei Neagoie and Daniel Bourke, Stephen Grider and Soledad Galli, has years of consistent feedback behind them. A single course from a publisher with no profile history is a bigger gamble.
Match the language to your goal
Machine learning is taught in Python, R and occasionally JavaScript. Python is the industry default and the safest bet for most careers; R is strong in statistics, research and academia; JavaScript is niche for ML but ideal for web developers who want the mechanics. Pick the course whose language matches where you are headed, then watch two of the free preview lectures before you pay, because audio and pacing are things no rating can tell you.
What Udemy Machine Learning Courses Actually Cost
Ignore the list price. Udemy courses list at up to about $200, but the platform discounts so often that few people pay it. On 1 November 2026 Udemy’s pricing page carried a new-learner banner reading “courses from $14.99”, and our top Python pick showed $14.99 against a $129.99 list price. If a course shows full price, wait a few days; our guides to how much Udemy costs and how often Udemy has sales cover the rhythm.
Each purchase is a one-time payment with lifetime access. Eligible purchases can be refunded within 30 days, but read the terms: Udemy says “certain restrictions may apply and some purchases may only be eligible for refunds in the form of credits,” and it may refuse a request when “a notable amount of the course has been consumed or downloaded” (Udemy’s refund policy). Our Udemy refund guide walks through the process.
The alternative is the Udemy Personal Plan, a subscription that starts at $13.00 a month (billed monthly or annually) for a library of more than 28,000 courses. All seven picks carry its “Subscribe and save” option. It wins only if you will work through two or more courses a month, which is realistic here if you take pick 1 and pick 7 back to back. Udemy states that subscriptions bought on Udemy.com “do not offer a 30-day satisfaction guarantee,” so for a single course, buy it outright.
Find your machine learning course on Udemy →
Which Udemy machine learning course should you take first?
Most people need one broad course, then one focused course. Here is the order we would follow by starting point.
| If you are… | Start with | Then take |
|---|---|---|
| New to programming | A Python course from our Udemy Python list (100 Days of Code or the Complete Python Bootcamp) | Machine Learning A-Z or the ZTM bootcamp |
| Comfortable in Python | Complete A.I. & ML, Data Science Bootcamp (ZTM) | Feature Engineering for ML, then TensorFlow 2 or PyTorch |
| An R user or statistician | Data Science & ML Bootcamp with R | Machine Learning A-Z, which repeats each method in Python |
| A JavaScript developer | Machine Learning with JavaScript | The ZTM bootcamp to move the same ideas into Python |
| Already training models at work | Feature Engineering for ML | A specialist course, such as NLP |
| Mainly interested in ChatGPT and LLMs | Our Udemy AI courses list | Come back here for the fundamentals |
Two warnings from the reader questions we see. First, do not stack three broad bootcamps; they overlap by more than half, and the second one teaches you little the first did not. Second, budget time, not money: the broad courses run 18 to 49 hours of video, which at five hours a week is two to ten weeks before the exercises you should be doing alongside.
Are there free machine learning courses on Udemy?
Yes. Udemy has a free filter for machine learning, and its own comparison on that listing separates free courses (online video content) from paid ones, which add a certificate of completion, instructor Q&A and direct messages with the instructor. None of the seven picks above is free, and none of the free ML courses we sampled comes close to their depth; most are short introductions published to promote a paid course.
If cost is the constraint, the better free route sits outside Udemy. Andrew Ng’s Machine Learning Specialization on Coursera can be previewed before you pay, and Coursera offers financial aid for the certificate; our Machine Learning Specialization review covers what is free and what is not. Then buy one Udemy course in a sale for the hands-on practice, which is where these picks are strongest.
Udemy vs Coursera for machine learning
The two platforms answer different questions. Udemy sells single courses from individual instructors, cheaply and forever. Coursera sells programs from universities and companies on a subscription, with graded assignments and a certificate carrying a recognizable name.
| Udemy (picks above) | Coursera | |
|---|---|---|
| Flagship ML course | Machine Learning A-Z, 4.5 from 206,721 ratings | Machine Learning Specialization (Stanford, DeepLearning.AI), 4.9 from 39,448 reviews |
| Format | Self-paced video, lifetime access | 3-course series, about 2 months at 10 hours a week, graded labs |
| Price model | One-time purchase; new-learner offer from $14.99 on 1 November 2026 | Subscription: $49 to $79 a month per program, or Coursera Plus |
| Certificate | Certificate of completion, not accredited | Shareable certificate from the program partner |
| Best for | Hands-on practice on a budget | A structured path and a recognizable name on a resume |
Our view: if you want to learn the material, a Udemy course in a sale is the cheapest credible way to do it. If you want a credential to show an employer, the Coursera specialization is worth paying for, and our best AI certifications guide ranks the recognized options. Many people do both: Coursera for the theory and the certificate, a Udemy bootcamp for the practice. For a cross-platform view, see our machine learning courses guide and best data science courses.
Frequently Asked Questions
What is the best Udemy course for machine learning?
For most people, Machine Learning A-Z by Kirill Eremenko and Hadelin de Ponteves (4.5 from 206,721 ratings, updated June 2026) is the best overall pick: it covers classical ML end to end in Python and R and is the most-reviewed ML course on Udemy. For a current, project-driven path in Python only, take the Complete A.I. & Machine Learning, Data Science Bootcamp by Zero To Mastery (4.7, updated February 2026).
Is Udemy good for learning machine learning?
Yes, for self-directed learners. The best ML courses on Udemy come from experienced instructors, carry tens of thousands of reviews and cost little in a sale, with lifetime access. The trade-off is no mentor, no graded projects and no accredited credential. If you need those, a Coursera program or a bootcamp fits better.
Should I learn machine learning in Python or R on Udemy?
Python is the industry default and the safer choice for most careers. R is strong for statistics, research and academic work. With no preference, choose Python: Machine Learning A-Z or the Zero To Mastery bootcamp. If you already work in R, the Data Science and Machine Learning Bootcamp with R (4.6) is the natural pick.
What is the difference between these and the best Udemy AI courses?
This guide covers traditional machine learning: algorithms, scikit-learn, supervised and unsupervised methods and the math behind them. Our separate best Udemy AI courses guide covers generative AI: ChatGPT, Claude, prompt engineering and building LLM applications. Learn the fundamentals here; use and build with current AI tools there.
How much do machine learning courses on Udemy cost?
List prices run up to about $200, but Udemy discounts constantly; on 1 November 2026 its new-learner offer read “courses from $14.99”. Each course is a one-time purchase with lifetime access. The Personal Plan subscription, from $13.00 a month, includes all seven picks and only beats buying if you take two or more courses a month.
Can I get a refund if a Udemy machine learning course is not good?
Usually. Eligible course purchases can be refunded within 30 days, but Udemy says some purchases may only be refunded as credits, and it may refuse requests when a notable amount of the course has been consumed or downloaded. Personal Plan subscriptions bought on Udemy.com have no 30-day guarantee.
Are there free machine learning courses on Udemy?
Yes, but they are short. Udemy lists free machine learning courses, and free courses come without the certificate of completion, instructor Q&A and direct messages that paid courses include. For a free route with depth, preview Andrew Ng’s Machine Learning Specialization on Coursera, then buy one Udemy course in a sale for hands-on practice.
Do Udemy machine learning courses give a certificate?
Every paid Udemy course issues a certificate of completion when you finish. It is not accredited: Udemy says its certificates cannot be used for formal accreditation. It can show initiative on a resume, but employers weigh projects and skills far more. For a recognized credential, see our best AI certifications guide.
How long does it take to learn machine learning on Udemy?
The broad courses here run from about 18 to 49 hours of video. At five hours a week, with time for exercises, plan on two to three months for one broad course and a focused follow-up such as Feature Engineering for Machine Learning.
Related: natural language processing is one of the most common specializations after a first machine learning course. Our guide to the best NLP courses compares the DeepLearning.AI specialization, Udemy options and free material.
Related Udemy & Machine Learning Guides
- Udemy review 2026: is the platform worth it
- Best Udemy AI courses: the generative-AI companion to this guide
- Best Udemy Python courses: learn the language ML runs on
- Best machine learning courses and tutorials: Coursera, edX and more compared
- Best Udemy courses overall: top picks across every subject
- Udemy Personal Plan review: subscribe or buy one course at a time
- Best AI certifications: the credential route when a Udemy course is not enough
Start with Machine Learning A-Z →