This guide is for anyone who wants to learn machine learning from the start and is deciding between one long course and getting the Python data basics down first. Every option is on Udemy, and I read every fact on the course page.
- Best overall: Machine Learning A-Z (Kirill Eremenko and Hadelin de Ponteves), 49.5 hours with more than 206,000 reviews.
- With data science included: Zero To Mastery's Complete A.I. & Machine Learning, Data Science Bootcamp, 44 hours.
- Learn the data basics first: Boris Paskhaver's Data Analysis with Pandas and Python, before any modeling.
| Course | Level | Length | View course |
|---|---|---|---|
| Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R Udemy Best overall | All levels | 49.5 h | View course |
| Complete A.I. & Machine Learning, Data Science Bootcamp Udemy With data science included | All levels | 44 h | View course |
| Data Analysis with Pandas and Python [2026] Udemy Learn the data basics first | All levels | 17.5 h | View course |
What you need to learn
Machine learning means teaching a program to work out its own rules from examples and then use them to make predictions. Almost every project goes through the same steps:
- Data: collect and clean the examples, usually with Python and pandas.
- Features: choose and prepare the columns the model will use.
- Training: fit an algorithm to the data, such as linear regression, a decision tree or k-means.
- Evaluation: measure accuracy on data the model did not see during training.
- Deployment: put the model to work on new data.
The problems beginners run into most are classification (predict a category), regression (predict a number) and clustering (find similar groups). A beginner course should cover all three and have you write code at every step.
I work in DevOps and machine learning is not my specialty, so my advice here is practical. Get a working Python environment before the first lesson, either on your machine or in a cloud notebook, so the first hours of study don't go to installing things.
Machine Learning A-Z: best overall
Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
Kirill Eremenko, Hadelin de Ponteves, SuperDataScience • Udemy
The most reviewed course I compared, with 49.5 hours of lessons and examples in both Python and R. It's for beginners who want one course that covers the whole path.
- Level
- All levels
- Length
- 49.5 h
- Certificate
- Completion
- Rating
- 4.5 (206k)
- Audio
- English
- Subtitles
- English; 28 more languages
Pros
- 49.5 hours of video, updated June 13, 2026
- 4.5 rating from more than 206,000 reviews
- Covers both Python and R
Cons
- Long: covering both languages adds hours you may not need
It was last updated on June 13, 2026 and is marked for all levels. Teaching two languages makes it longer. If you only plan to use Python, you can skip the R lessons. The title also promises deep learning and deployment on AWS, so it goes past the basics.
Zero To Mastery: with data science included
Complete A.I. & Machine Learning, Data Science Bootcamp
Andrei Neagoie, Daniel Bourke (Zero To Mastery) • Udemy
44 hours from Andrei Neagoie and Daniel Bourke that combine data analysis, data science and machine learning. It's for people who want the data work and the modeling in one bootcamp.
- Level
- All levels
- Length
- 44 h
- Certificate
- Completion
- Rating
- 4.6 (31k)
- Audio
- English
- Subtitles
- 12 languages
Pros
- 44 hours covering data analysis and data science along with machine learning
- 4.6 rating from about 31,000 reviews
- Updated February 19, 2026
Cons
- No Portuguese subtitles
It was updated on February 19, 2026 and has about 31,000 reviews with a 4.6 rating. It uses Python only. There are no Portuguese subtitles, which matters only if you rely on them.
Boris Paskhaver: learn the data basics first
Data Analysis with Pandas and Python [2026]
Boris Paskhaver • Udemy
17.5 hours on pandas, the library you use to load and clean data before any model. It's for people who find the data step in the ML courses too fast.
- Level
- All levels
- Length
- 17.5 h
- Certificate
- Completion
- Rating
- 4.7 (27k)
- Audio
- English
- Subtitles
- English; 27 more languages
Pros
- 17.5 hours focused on pandas, updated July 28, 2026
- 4.7 rating from about 26,700 reviews
Cons
- Centered on pandas: charts and machine learning are not the focus
This one has no modeling. It covers the first step of the path above, which is where many beginners get stuck. It was updated on July 28, 2026 and has about 26,700 reviews. My guide to Python for data compares it with other pandas courses.
How I chose
I used the same methodology as every other guide. For this topic four things mattered: entry level, recent updates, hands-on Python code, and coverage of the full path from data to evaluation. For now I only compare courses on Udemy. I read every fact on the course page on September 28, 2026.
I left out two courses by Jose Portilla that have many reviews: Python for Machine Learning & Data Science Masterclass, not updated since December 2022, and Python for Data Science and Machine Learning Bootcamp, not updated since May 2020.
Frequently asked questions
Do I need to know how to code to start machine learning?
It helps, but it is not required. The courses here use Python, and the beginner ones explain what you need. If you have never coded, start with a pandas course like Paskhaver's.
How much math do I need?
Not much to start. Beginner courses explain the idea behind each algorithm without calculus. Basic statistics helps with the evaluation step.
Python or R?
Every course here uses Python. Machine Learning A-Z teaches both, but you can follow only the Python lessons.
What is the difference between machine learning and deep learning?
Deep learning is a part of machine learning that uses neural networks with many layers. The usual advice for beginners is to start with classic machine learning and move to deep learning afterwards.