Python Machine Learning Courses Worth Taking
There are hundreds of machine learning courses in Python. These are the few worth the hours, the ones that get you training real models instead of memorizing terms.
Courses in this collection
Start with the shortlist, then see how they differ side by side further down.
How they actually differ
What the list leaves out, from who each course is for to what sets it apart.
Machine Learning using Python on Udemy | Understanding Machine Learning with Python 3 on Pluralsight | Supervised Learning with scikit-learn on Datacamp | A Practical Guide to Machine Learning with Python on Educative | Machine Learning with Python on Coursera | Introduction to Machine Learning with Pytorch on Udacity | IBM: Machine Learning with Python: A Practical Introduction on edX | |
|---|---|---|---|---|---|---|---|
| Best for | Self-starters beginning from zero who want the widest coverage in one purchase | Working developers who want a quick video overview of the whole ML workflow before committing to something deeper | People who want to drill the supervised-learning core through hands-on browser exercises | Learners who would rather read and run code than watch videos | People who want the graded IBM version with ensemble methods and a capstone, ending in a career certificate | Learners with a real math and Python background ready for a longer, reviewed, project-based program | People who want the same IBM material at an introductory level, free to audit |
| Differentiator | The broadest scope and the only course teaching time-series forecasting (ARIMA/SARIMA), with no prerequisites | The shortest video course, aimed at people who already program | Interactive code-in-the-browser exercises with a deliberately narrow supervised-only scope | The only fully text-based, no-video course, with an in-browser code editor | The intermediate IBM course with graded work, Random Forest/XGBoost, and a final project plus exam | The only option covering deep learning with PyTorch and mentor-reviewed portfolio projects | The introductory IBM course, the only one teaching recommender systems, but without the ensembles or capstone |
| Price | $74.99 | Pluralsight subscription | Datacamp subscription | Educative subscription | Coursera Plus subscription | Udacity subscription | edX subscription |
| Rating | 4.5 | 4.5 | 4.8 | 4.7 | 4.7 | 4.7 | — |
| Offered by | — | — | — | — |  IBM | — | IBM |
| Prerequisites | None | Basic programming and statistics | Basic statistics in Python | Basic Python knowledge | Basic Python knowledge | Intermediate Python, math, and ML basics | None |
| Includes | 2 role plays18.5 hrs video5 coding exercises10 articles126 downloadable resourcesmobile accessTV access | 1.9 hrs video | 15 videos49 Exercises | 57 lessons2 projects12 quizzes | 6 modules32 videos17 assignments | 12 courses74 lessons3 projects | — |
| Certificate | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Taught in | English | English | English | English | English, Swedish (auto), Korean (auto) and 23 more languages | English | English |
| Subtitles | English (auto), Hindi (auto), Korean (auto) and 1 more language | — | — | — | Bangla, Persian, Urdu | — | English |
| Access | Lifetime | Subscription | Subscription | Subscription | Subscription | Subscription | Lifetime |
| Duration | 18 hours and 50 minutes | 1 hour and 54 minutes | 4 hours | 72 hours and 30 minutes | 20 hours | 49 hours | 5 weeks (4-6 hours per week) |
| Skill level | All Levels | Beginner | Intermediate | Beginner | Intermediate | Intermediate | Beginner |
The full view adds total enrolled, total reviews and when each course was last updated.
What to check before you start machine learning with Python
Machine learning with Python is a turning point. The language stops being something you study and becomes a tool you build with. The catch is that it rests on a considerable amount of other Python. Before models make sense, you want to be comfortable with fundamental Python and with manipulating data in pandas and NumPy. A course that has to stop and teach those first is really two courses in one. If you are not there yet, start with the fundamentals and the data-handling side, then come back.
Once the groundwork is in place, almost every course here runs on the same library, scikit-learn. They progress through the classical models first: regression, classification, and clustering. Then comes the element that matters most professionally, determining whether a model actually performs. Where they diverge is scope and depth. Some stay tight on the supervised-learning core. Others add unsupervised methods or time-series forecasting, and one carries you into neural networks. Be straight with yourself about the maths too. You do not need a maths degree, but familiarity with statistics carries you further here than anywhere else in Python. The more demanding courses assume it going in.
What is the best way to learn machine learning in Python?
Build and evaluate real models yourself rather than watching someone else do it. The strongest courses get you into scikit-learn early. They explain the reasoning behind each model, not just the code that runs it. Real datasets replace toy examples. And they refuse to quietly bypass the maths to appear more approachable. Treat evaluation as seriously as training, since knowing whether a model works is the part the job depends on. Applied machine learning also arrives in more formats than people expect. They range from short interactive exercises to longer project-based programs with reviewed work. Pick the one that matches how you learn.
Should you learn Python before machine learning?
Yes, at least the basics. These courses assume you can already read and write Python and move data around. The whole thing runs on libraries you operate through Python. People who skip straight to the models tend to stall when something breaks. They cannot tell whether the fault is the model or the Python holding it up. Get the language down first and add some data handling. After that, learning machine learning with Python becomes about ideas rather than a fight with syntax. If you are aiming further out, deep learning and neural networks build on all of this. That is the natural next step once the classical models click.
The steps around machine learning
ML sits on top of Python and data science, so make sure those are solid. From there, AI programming is the next step up.
9 courses
6 courses
6 courses