Machine Learning A-Z Review (2026): Is Udemy’s Best-Selling ML Course Still Worth It?

Search for a machine learning course and one name outranks the rest by sheer weight of numbers. Machine Learning A-Z has 4.5 stars from more than 206,000 ratings and over 1.2 million learners. Numbers like that attract two kinds of people: beginners who want the safe choice, and skeptics who assume a course that popular must be shallow. This Machine Learning A-Z review is for both.

We went through the 2026 edition section by section, including the new AWS track that replaced the old ChatGPT bonus. Below you will find what the course actually teaches and where its explanations stop short. This Machine Learning A-Z review also covers what it costs on sale, and which alternative fits if it is not the right one for you.

Machine Learning A-Z is a video course on Udemy by Kirill Eremenko and Hadelin de Ponteves, the pair behind SuperDataScience and the Ligency team. Its full 2026 title reads Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python and R. Older write-ups still call it Machine Learning A to Z, or the Hands-On Python and R in Data Science course, because that was the original name.

The course runs 49 hours across 50 sections and 474 lectures, and it was last updated in June 2026. It is structured as 13 parts. The first ten cover data preprocessing, regression, classification, clustering, reinforcement learning, NLP and deep learning. The last three build and deploy models on AWS. You can follow the Python track, the R track, the AWS track, or all three.

Like every Udemy course, it ends with a certificate of completion rather than an accredited credential. Buy it once and you keep lifetime access, including future updates.

The course fits people with high school math and a little programming who want a broad first tour of machine learning. Career changers, analysts who want to move beyond spreadsheets, and students preparing for a data science role get the most from it.

It fits two groups poorly. If you want to build products on large language models or AI agents, the course barely touches that world. And if you already know scikit-learn, the pace will feel slow. In both cases, the alternatives section below points you somewhere better.

Every part follows the same rhythm. First, Kirill explains the intuition of an algorithm on a whiteboard, with no equations beyond what a high school student can follow. Then Hadelin implements it in a short notebook, in Python and again in R. Since 2026, a third instructor voice covers the AWS parts. Here is how the three blocks hold up.

Parts 1 to 10 are the course people bought a million times. Data preprocessing comes first, then regression: simple, multiple, polynomial, SVR, decision trees and random forests. Classification follows with logistic regression, K-NN, SVM, naive Bayes and trees. After that come clustering, association rules, reinforcement learning with Thompson sampling, and NLP with a bag-of-words model. Deep learning closes the block with artificial and convolutional neural networks.

The breadth is the selling point. However, breadth has a cost. Each algorithm gets an intuition lesson and one worked example, and then the course moves on. As a result, you finish with a strong mental map of the field and a thin grasp of the math underneath it. Part 10, on cross validation, grid search and XGBoost, is where the course finally shows how practitioners choose between models.

The 2026 update added three parts on Amazon Web Services. Part 11 covers data preparation with S3, Glue, DataBrew and SageMaker Data Wrangler. Part 12 builds XGBoost, LightGBM and CatBoost models in SageMaker and tours the managed AI services: Comprehend, Rekognition, Polly, Transcribe, Textract and Translate. Part 13 deploys a model and compares real-time, serverless and asynchronous inference.

This track is the strongest argument for buying the course again if you already own it. Deployment was the biggest gap in the old edition, and no other beginner ML course on Udemy covers it this concretely. Keep in mind that the AWS lessons need an AWS account, and some services cost real money beyond the free tier.

Every algorithm ships as a code template in Python and R. In practice, most learners now skip R, and the course is fine with that. The Python notebooks run in Google Colab with no setup, which removes the classic first-week frustration of installing libraries.

The templates are also the most common complaint in reviews. Because the code is prewritten, the exercises can turn into reading along rather than writing. If you want the course to stick, close the template, rebuild each model from a blank notebook, and only then compare. The scikit-learn user guide is the natural companion for the math the videos skip.

So, how good is Machine Learning A-Z in practice? Very good at what it promises and honest about what it does not. After the full 49 hours you can preprocess a dataset, pick a sensible model, tune it with grid search and explain to a colleague why it works. That is more than most beginners can do after a semester.

What you will not have is depth. The math stays at intuition level, the datasets are small and clean, and the projects are guided rather than open ended. Reviewers on Udemy and on third-party sites repeat the same two points, and this Machine Learning A-Z review kept coming back to them too: excellent intuition, weak exercises. The 2026 AWS track is the exception, since deploying to SageMaker is a genuinely practical skill.

Two smaller issues remain. A few older library calls still appear in the notebooks, and the R half of the course dates it. Neither breaks the learning, but both remind you that this is a course first recorded years ago and patched since.

Machine Learning A-Z is a one-time purchase on Udemy, not a subscription. The list price is $159.99, but Udemy runs sales almost every week, and the course dropped to $12.00 during our check in September 2026. Never pay the list price. Here is what the options look like.

Is Machine Learning A-Z free? No. Udemy lets you watch a handful of preview lectures, and the course is included in the Personal Plan subscription, but there is no free full edition. A 30-day refund window applies to the one-time purchase, which is generous enough to test the first few sections. For this Machine Learning A-Z review the price was checked on the US store, and Udemy shows regional prices, so yours may differ.

The other course beginners shortlist is Andrei Neagoie and Daniel Bourke’s Complete A.I. and Machine Learning, Data Science Bootcamp, which we rank as the best all-rounder in our best AI courses on Udemy guide. The two overlap a lot, so the choice comes down to style.

Machine Learning A-Z is wider. It covers association rules, reinforcement learning and the AWS track, which the bootcamp skips. The bootcamp is more project driven, sticks to Python, and spends more time on pandas, NumPy and the workflow of a real data scientist. In short, take Machine Learning A-Z for the broadest map of algorithms, and the bootcamp if you want to build a portfolio in Python only.

Is Machine Learning A-Z legit? Yes. It has been on Udemy since 2016, it is a Udemy Bestseller, and its 206,000 ratings average 4.5 out of 5. The instructors run SuperDataScience, one of the longest running data science education companies, and the course has received regular updates, most recently in June 2026.

Expect a certificate of completion, not a qualification. Employers treat it as evidence that you studied, the same way they treat any Udemy certificate. If you need a credential a recruiter recognizes, pair the course with a real exam. Udemy now sells discounted certification exam vouchers for the AWS Certified Machine Learning Engineer exam, at $157.50 instead of $180. For this Machine Learning A-Z review that pairing stood out, because the AWS track prepares you for exactly that exam.

Artificial Intelligence A-Z is the sequel by the same instructors. It skips classic ML and goes straight to deep learning, reinforcement learning and, in the 2026 edition, LLMs and agentic AI. Take it after Machine Learning A-Z, not instead of it, unless neural networks are your only goal.

Andrew Ng’s Machine Learning Specialization on Coursera is the academic counterpart. It goes deeper into the math and comes with a Coursera certificate, but it costs a monthly subscription rather than a one-time $12, and it moves slower. Choose it when theory and a recognized certificate outrank speed.

DataCamp teaches the same algorithms in short, interactive browser exercises with instant feedback. It fixes the biggest weakness of Machine Learning A-Z, the passive code templates, but it is a subscription, and its explanations are shorter. It suits learners who learn by typing rather than watching.

Pros

• The widest algorithm coverage of any beginner ML course on Udemy, from regression to reinforcement learning
• Intuition-first explanations that make each model click before any code appears
• A 2026 AWS track that covers data preparation, SageMaker training and real deployment
• Python and R templates that run in Google Colab with no setup
• 4.5 stars from over 206,000 ratings and updates as recent as June 2026
• Costs $12 to $20 on sale for lifetime access, with a 30-day refund window

Cons

• Math stays at intuition level, so you will need another source for the theory
• Prewritten templates turn many exercises into reading along instead of coding
• Datasets are small and clean, unlike anything you will meet at work
• Almost nothing on large language models, transformers or AI agents
• The R track and a few older library calls show the course’s age
• The AWS parts need an AWS account and can incur small cloud charges

So, is Machine Learning A-Z worth it? Yes, at the sale price, as your first machine learning course. No other course gives a beginner a map of the whole field this clearly for the cost of a lunch. In addition, the 2026 AWS track adds the deployment skills the old edition lacked.

It earns 4.2 out of 5 in this Machine Learning A-Z review. It loses points for the light math, the passive templates and the missing LLM content, not for anything it does badly. Buy it on sale, do every exercise from a blank notebook, and move to Artificial Intelligence A-Z or a specialization once the map is in your head. That is still the most efficient $12 a beginner can spend on machine learning.

See how this course ranks against the other bestsellers in our best AI courses on Udemy collection. For the platform itself, read our full Udemy AI courses review, which covers pricing, free options and the new exam vouchers. Starting from zero? Our best AI courses for beginners list covers every platform.