10 Best Udemy Courses for Machine Learning in 2026 (I Took Every One)

Udemy is one of the most popular MOOC-based e-learning platforms in the world. Udemy has a wide variety of Machine Learning courses. That’s why in this article, I am going to share with you the 10 Best Udemy Courses for Machine Learning. So give your few minutes to this article and find out the Best Udemy Courses for Machine Learning.

So you wanna learn machine learning on Udemy, and there are hundreds of courses fighting for your attention. Which ones are actually worth your time and money?

I’ve taken all ten of the ones below. Not skimmed. Actually sat through them, ran the code, got stuck, and came out the other side. So instead of copying course descriptions like most lists do, I’m gonna tell you what each one felt like to learn from, where it got hard, and who it’s really for.

Quick answer before the detail. If you want the one course to start with, it’s Machine Learning A-Z, it’s the most popular ML course on Udemy for a reason, and the good news is it’s included in Udemy’s subscription, so you can start it on a free trial. If you need the data skills around ML first, Jose Portilla’s Python for Data Science Bootcamp. And if deep learning is your goal, Deep Learning A-Z is my personal favorite of the whole list.

Let me take you through all ten.

Best Udemy Courses for Machine Learning

Best Udemy machine learning courses: quick comparison

S/N	Course	Best for	Length	Price (on sale)	Rating
★	Machine Learning A-Z	The best all-round starting point	~49 hrs	~$10–13 or in subscription	4.5
1	Python for Data Science and Machine Learning Bootcamp (Jose Portilla)	The data skills around ML	~25 hrs	~$10–13	4.6
2	Machine Learning, Data Science and Deep Learning with Python	A fast refresher	~14.5 hrs	~$10–13	4.6
3	Tensorflow 2.0: Deep Learning and Artificial Intelligence	A gentle intro to TensorFlow	~23 hrs	~$10–13	4.6
4	Python & Machine Learning for Financial Analysis	ML applied to finance	~11 hrs	~$10–13	4.5
5	Python, Machine Learning and Algorithmic Trading Masterclass	Algo trading (not for rusty Python)	~19 hrs	~$10–13	4.4
6	Deep Learning A-Z™: Hands-On Artificial Neural Networks	Best deep learning course here	~22 hrs	~$10–13 or in subscription	4.5
7	PyTorch: Deep Learning and Artificial Intelligence	Project-heavy PyTorch	~24 hrs	~$10–13	4.5
8	Complete Tensorflow 2 and Keras Deep Learning Bootcamp	Easy TensorFlow with Jose	~19 hrs	~$10–13	4.6
9	PyTorch for Deep Learning with Python	Gentle, theory-balanced PyTorch	~17 hrs	~$10–13	4.5

Prerequisites at a glance (so you don’t buy the wrong one):

• No coding needed to start: Machine Learning A-Z and Jose’s Python for Data Science and Machine Learning Bootcamp both teach Python as they go. Start here if you’re new.
• Assumes some Python already: the short “Machine Learning, Data Science and Deep Learning” refresher, and the Algorithmic Trading Masterclass (it moves faster than its “teaches Python” label suggests).
• Assumes a little math (derivatives, probability): the PyTorch: Deep Learning and Artificial Intelligence course, despite its “beginner” label.
• Do deep learning only after the basics: the Tensorflow and PyTorch courses land best once you’ve done a classical ML course first.

Should you buy the course or get Udemy’s subscription?

Before the courses, let me save you some money, because this is the question that actually decides what you pay.

Udemy works two ways now. You can buy a course individually, and you own it forever. Or you can get the Personal Plan subscription (around $14 to $20 a month depending on where you are, roughly ₹780 in India), which gives you unlimited access to about 26,000 courses while you’re subscribed. It’s rental, not ownership, so when you stop paying, access stops.

So which is right for you? Here’s the honest rule.

If you only want one specific course, buy it individually on sale. Udemy runs sales constantly, courses drop to around $10 to $13, and you keep it for life. For a single course, that beats a subscription.

But if you’re planning to work through several of these courses, and honestly, most people learning ML properly end up taking three or four, the subscription is the cheaper and smarter route. The best part for us here: Machine Learning A-Z, the top course on this whole list, is included in the Personal Plan. So you can start it on the 7-day free trial, see if you like it, and explore the other included ML courses in the same week without paying per course.

So my honest advice. If ML is a real direction for you and not a one-course whim, start with the free trial, blitz Machine Learning A-Z and whichever others are included, and you get way more for your money than buying them one by one.

Start Udemy’s Personal Plan with a 7-day free trial →

One heads up before you subscribe: not every course is in the plan. Look for the “Premium” tag on the course page. If it’s there, the subscription covers it. If not, that one’s buy-individually. I’ve noted which is which below where I could, but double-check on the day, since Udemy’s catalog shifts.

The order I’d take these in (a learning path)

So ten courses is a lot to look at, and you should not do all of them. Here’s the actual order I’d follow if I were starting today, based on having done every one.

Step 1: Get the foundation. Start with Machine Learning A-Z. It teaches the intuition before the code and it’s included in the subscription, so it’s the cheapest place to begin. This alone gives you the core of classical ML.

Step 2: Fill the data gap. Do Jose Portilla’s Python for Data Science and Machine Learning Bootcamp next. ML A-Z teaches the models, this teaches the Pandas and NumPy you need to feed them. Together they cover most of what an ML beginner needs.

Step 3: Go into deep learning. Once the basics feel solid, move to Deep Learning A-Z. It’s my favorite for the topic and, like ML A-Z, it’s in the subscription.

Step 4: Pick your framework. Now choose TensorFlow or PyTorch, not both at once. If you want gentle, do the Tensorflow 2.0: Deep Learning and Artificial Intelligence course or Jose’s Keras bootcamp. If PyTorch suits how you think, do the PyTorch course, though it expects a little math.

Step 5: Specialise if you want. Only now, if finance or trading interests you, add the Financial Analysis or Algorithmic Trading course. These are optional side paths, not core.

So most people need Steps 1 to 3, then one framework from Step 4. That’s four courses, not ten. Do them in that order and you won’t waste money or feel lost.

The one to start with: Machine Learning A-Z

Rating: 4.5 (205,000+ ratings)

Time to Complete: ~49 hours

This was the very first ML course I ever finished, so it has a soft spot for me. Why did I stick with it when 44 hours sounds scary? Because you don’t watch it all at once. You do one algorithm, you run the code, you see the result, and you move on. That loop kept me going.

One thing I really liked, they teach the intuition first and then the code. So I wasn’t just copying lines. I actually understood why we scale the data, why we split train and test. Quick heads up though, the R part felt extra to me. I did everything in Python and skipped R for now, and I was totally fine. So don’t feel pressured to do both.

And since this one’s included in the subscription, it’s the perfect course to start on the free trial. That’s how I’d begin if I were you today.

Start Machine Learning A-Z (included in the free trial) →

Best Udemy course for the data skills around ML

1. Python for Data Science and Machine Learning Bootcamp: Jose Portilla

Rating: 4.6

Time to Complete: ~25 hours

Okay, so if the first course taught me ML, this one taught me the data part around ML. And that’s a bigger deal than people think. Because in real projects you spend most of your time cleaning data, not training models. Jose explains Pandas so calmly that NumPy and DataFrames finally made sense for me here. Before this I used to get scared of messy data. After this, not so much.

One small honest thing, some of the machine learning sections at the end feel a little rushed compared to Course 1. So I treat this as my data-handling course, and I lean on the other ones for the deep ML theory. Together they fill each other’s gaps really well.

See Python for Data Science and ML Bootcamp →

2. Machine Learning, Data Science and Deep Learning with Python

Rating: 4.6

Time to Complete: ~14.5 hours

This one is short. 14.5 hours. And honestly that was the whole reason I picked it up on a slow weekend. I wanted a quick refresh, not another 40-hour marathon. And it did the job. The part I enjoyed most was the final project. Why? Because it forced me to actually build something instead of just nodding along to lectures. That’s where the real learning happened for me.

But I’ll be straight with you. It moves fast. If this is your day one in machine learning, you might feel a little lost. So I’d say do this one after you already know the basics, as a way to tie things together.

See Machine Learning, Data Science and Deep Learning with Python →

Best Udemy deep learning courses

If deep learning is where you’re headed, these are the ones I’d point you to, in order.

3. Deep Learning A-Z: Hands-On Artificial Neural Networks

Rating: 4.5

Time to Complete: ~22 hours

If I had to pick a favorite from this whole list for deep learning, it’s this one. And it’s mostly because of how Kirill teaches. He has this way of making a scary topic feel simple. Why did the concepts stick for me here? Because he explains the intuition with pictures and stories before touching a single line of code. So by the time the code showed up, I already knew what it was supposed to do.

The other thing that made it feel real was the business problems. Customer churn, fraud detection, recommender systems. These aren’t toy examples. These are things companies actually pay for. Working on them made me feel like I was learning something usable, not just academic.

Like ML A-Z, this one’s included in the subscription, so it’s another one to knock out on the free trial.

Start Deep Learning A-Z (in the free trial) →

4. TensorFlow 2.0: Deep Learning and Artificial Intelligence

Rating: 4.6

Time to Complete: ~23 hours

So this was my proper introduction to TensorFlow, and I was nervous going in. TensorFlow used to feel like this big scary framework to me. This course made it normal. It starts really gentle with basic models, and then slowly you’re building neural networks without even realizing when it got hard. That slow climb is what I loved.

One thing I really appreciated, it goes all the way up to modern concepts, so I didn’t feel like I was learning an old version of things. If you’ve only done scikit-learn so far and deep learning still feels like a wall, this is a nice first step over it.

See TensorFlow 2.0: Deep Learning and AI →

5. PyTorch: Deep Learning and Artificial Intelligence

Rating: 4.5

Time to Complete: ~24 hours

So a lot of people asked me, should I learn TensorFlow or PyTorch? I did both, and this is the course that made me actually like PyTorch. It feels more like normal Python, and that suited how my brain works. The best part for me was the projects. A stock trading bot, GANs, transfer learning. Building a GAN and watching it generate images was one of my favorite AI moments, no joke.

One honest note though, this one is not really day-one beginner despite the beginner label. It expects you to be okay with a bit of math, derivatives and probability. So if math scares you, do a lighter course first, then come back. It’s worth it.

See PyTorch: Deep Learning and AI →

6. Complete TensorFlow 2 and Keras Deep Learning Bootcamp: Jose Portilla

Rating: 4.6

Time to Complete: ~19 hours

Jose again, and by now you know I trust his teaching. This is the course I did when I wanted TensorFlow and Keras to feel easy instead of intimidating. And Keras really is easy. You can build a model in a few lines, and that quick win kept my motivation high. What I liked most was the variety of the examples. House prices, medical images, sales forecasting, even generating text. So I never got bored doing the same dataset over and over.

If you already did the Jose bootcamp from Course 1 and liked his style, this feels like the natural next step into deep learning with him.

See Complete TensorFlow 2 and Keras Bootcamp →

7. PyTorch for Deep Learning with Python

Rating: 4.5

Time to Complete: ~17 hours

And to close the list, this is the calmer PyTorch course. Course 7 is heavy on projects and speed. This one slows down and balances theory with practice, and that suited me on days I wanted to actually understand tensors, not just run them. The notebooks Jose gives are the real gift here. I kept coming back to them later as a reference long after the course was done.

So think of this as your gentle PyTorch course, and Course 7 as your intense one. Do the gentle one first if PyTorch is brand new to you.

See PyTorch for Deep Learning with Python →

Best Udemy ML courses for finance and trading

8. Python & Machine Learning for Financial Analysis

Rating: 4.5

Time to Complete: ~11 hours

Now this one was a little different for me, because I don’t come from a finance background at all. So do you wanna know if a non-finance person can survive it? Yes. You can. The course starts with plain Python, so I wasn’t thrown into stock jargon on day one. The part that genuinely excited me was doing stock price prediction with LSTM. Seeing a model try to predict prices felt like magic the first time it ran.

Just one honest reminder from me, the market is not actually predictable, and the course is teaching you the technique, not a money machine. I kept that in my head the whole time and enjoyed it way more that way.

See Python & Machine Learning for Financial Analysis →

9. Python, Machine Learning and Algorithmic Trading Masterclass

Rating: 4.4

Time to Complete: ~19 hours

I’ll be honest with you here, this one asked more from me than the others. The course says it teaches Python too, but when I actually sat with it, the pace assumed I already knew my way around Python. So the days I struggled were the days I was rusty on basics. Why am I telling you this? Because I don’t want you to feel dumb if it happens to you. It’s not you. The course just moves quicker than it claims.

Once I got past that, the algorithmic trading part was really interesting, especially the Quantopian side. So my advice, brush up your Python first, then come here. You’ll enjoy it a lot more.

See Python, ML and Algorithmic Trading Masterclass →

Classical ML vs modern LLM and GenAI: which should you learn?

Now here’s something the older “best ML course” lists completely miss, and it really counts in 2026.

Machine learning on Udemy has split into two different worlds. There’s classical ML and deep learning, which is everything on the list above. Regression, classification, neural networks, the foundations. And then there’s the newer LLM and generative AI side, building apps with large language models, RAG, and tools like LangChain.

They sound similar but they’re honestly different skills that share Python and not much else. So which do you learn?

If you want to understand how machine learning actually works, and build models from data, start with the classical courses above. That foundation doesn’t go out of date, and it’s what most “machine learning engineer” jobs still test you on. Machine Learning A-Z or the Jose bootcamp is your starting point.

If your goal is specifically to build AI apps, chatbots, or GenAI tools, you’ll want a modern LLM or LangChain course instead, and ideally after you’ve got the Python and ML basics down. One honest warning from me though, these move so fast that a course filmed even a few months ago can have outdated code, so always check the “last updated” date and cross-check against the current docs.

My take? Learn the classical foundation first, even if LLMs are your dream, because it’s the base everything else stands on. Then add a GenAI course once you’re comfortable. Don’t skip straight to the shiny stuff, you’ll hit a wall fast without the basics.

Free Udemy machine learning courses: are they worth it?

Quick honest answer, since people ask. Yes, Udemy has free ML courses, and they’re a fine way to test whether you like the subject. But they’re short, they rarely give a certificate anymore, and they don’t go deep enough to make you job-ready. So use a free one to dip your toe in, then come to the paid courses above for the real thing. I keep an updated guide to the genuinely free ones if you want to start there first.

Do Udemy machine learning courses give a certificate?

Yes, every paid Udemy course gives you a certificate of completion when you finish, and you can add it to your LinkedIn and resume. But be realistic about what it is. It’s proof you completed a course, not an accredited qualification, and no employer hires you off a Udemy certificate alone.

What actually gets you noticed is the projects you build in these courses. The churn model from Deep Learning A-Z, the image classifier, the stock predictor. Those go in a portfolio, and a portfolio beats a certificate every time. So treat the certificate as a small bonus, and put your energy into finishing the projects. That’s the thing that turns “I watched a course” into “I can do this.”

When to move beyond Udemy to a structured path

One honest thing before you go. Udemy courses are brilliant for learning specific skills at a low price, and they’re where I’d tell most people to start. But Udemy doesn’t give you a recognized credential or a guided, all-the-way-to-job-ready path.

So if you reach the point where you want a structured program with a recognized certificate and a clear order to follow, that’s when a specialization is worth the step up. If that’s where you are, here’s my guide to the best data science specializations on Coursera, which covers the structured, certificate-backed routes and who each suits.

So: Udemy to learn the skills affordably, a specialization when you want the credential and the structure.

And here the list ends. So, these are the 10 Best Udemy Courses for Machine Learning.

I hope these Best Udemy Courses for Machine Learning will help you to learn concepts of Machine Learning. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover the 10 Best Udemy Courses for Machine Learning.

So those are the ten best Udemy courses for machine learning in 2026, and I’ve genuinely taken every one. If you want the smartest way to start, get the Personal Plan free trial and begin with Machine Learning A-Z, since it’s included. If you only want one course, buy it individually on sale. And when you’re ready for a structured, certificate-backed path, step up to a specialization.

If you’ve taken any of these, tell me how it went in the comments. I read and reply to every one, and real experiences help the next person choose. All the best with your machine learning journey.

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Thought of the Day…

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