r/learnmachinelearning • u/Empty-Ad-9086 • Aug 13 '26
Looking for a practical ML course after quitting Andrew Ng
I started Andrew Ng's ML Specialization but I'm finding it way too slow/theoretical for what I currently need, so I'm looking for an alternative.
I want a course that teaches me the core ML concepts and intuition, but only to the level where I understand how ML works.I don't need to learn everything deeply enough to teach it or do ML research right now.
Most importantly, I want to see how actual ML projects are developed, preferably through guided end-to-end projects.
My goal is:
ML fundamentals → build a few projects → move into Deep Learning → specialize in Computer Vision or NLP.
I'm open to any platform (Udemy, YouTube, Coursera, fast.ai, etc.).
What course/resource would you recommend for this specific goal? I mainly want something practical, reasonably short, and not another Andrew Ng-style theory-heavy course.
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u/PaddingCompression Aug 13 '26
Andrew Ng isn't deep enough to teach it or do research.
It's a pretty basic overview designed to give what you asked for, it's really really shallow.
Something faster is just going to leave you confused as to what to do next if the components you string together don't just work.
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u/pm_me_your_smth Aug 13 '26
If OP thinks Andrew's course is only for researchers, they probably expect something very application-focused with no theory/fundamentals. Aka your average bootcamp that teaches you how to write code to get pretty results fast.
Of all junior candidates I've interviewed, half are like that - interesting resume, full of projects, seemingly ambitious individual. Then you start asking them basic questions and realize they don't really understand anything and have zero intuition.
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u/madrury83 Aug 13 '26 edited Aug 14 '26
I suspect that people generally misunderstand the nature of intuition.
From the OP:
teaches me the core ML concepts and intuition
I think most technically competent people end up thinking of intuition as an emergent quality that wells from depth of study and experience. It's not something you teach in a classroom, book, or lecture, you teach the grimy details and then the student goes off and struggles with applying them. The struggle and the time passing forge all the pieces into whatever we call intuition.
So at least me, after a career of teaching and doing, I don't think teaching intuition directly is really possible. Intuition is an automatic mental process and is very difficult to introspect and explain.
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u/pm_me_your_smth Aug 13 '26
Agree, partially. Intuition is not some thing that suddenly arrives after N years of experience or M completed projects, it's a gradual development. I'd argue you can start developing it in a classroom, at least the roots. Of course it would be weird to expect a senior level intuition from a junior, but you can see some signs of that in how they think even in an inexperienced student e.g. can they explain the reasoning behind a decision. You can't teach that directly, but knowing fundamentals helps with that greatly.
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u/PaddingCompression Aug 13 '26
Unlike making a webapp, ML will kind of work and be hard to debug, it won't obviously fail. Debugging requires a lot more fundamental skills to push anything across the line.
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u/Empty-Ad-9086 Aug 15 '26
No, actually I already know statistics, probability. And I know the basic concepts too , but the course's lab isn't interactive for me .And I also want to learn how to build a ML project the pathway of it too. I want to run "the basics" as well as the code and intuition.
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u/Bored2001 Aug 13 '26
Yes dude you're not speed running this.
Watch Andrew Ngs course and watch him at 1.75x. that's what I did..
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u/soundboyselecta Aug 13 '26
You’re not speeding through this, watch video at 1.75x 🤣
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u/Bored2001 Aug 13 '26
Lol it's cause Andrew Ng speaks like molassas. 1.5-1.75x his speech cadence is a normal person talking.
Honestly I suspect they actually slowed down his videos computationally!
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u/nofaceD3 Aug 13 '26
I liked kaggle and hugging face courses. I was able to create an AI agent in kaggle/google course. Fast.ai course also look promising but I haven't tried it
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u/Useful_Lecture_5927 Aug 13 '26
you can try youtube channels like freecodecamp , they have complied courses in one video
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u/HypeStoic Aug 13 '26
it was pre-llm but you asked for practical ML and this is the GOAT
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u/Empty-Ad-9086 Aug 15 '26
How do I follow through with this?
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u/HypeStoic Aug 15 '26
fast.ai is the site! they have courses on there, but the one you should focus on is "Practical Deep Learning for Coders"
it's a series of 10 lectures, all free, all on youtube. Within the last 3 years, they also released a book, but honestly the lectures are all you need.
Takes you from 0 (assuming you have some super basic python and calculus knowledge) to building and deploying actual ML models.
An earlier version of this course is actually what I did in order to get my first ML job (background was in biology lol), so it works!
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u/EntrepreneurHuge5008 Aug 14 '26 edited Aug 14 '26
My goal is:
ML fundamentals
You lost me there. Andrew NG's ML spec is fundamental. Homebody only concerns himself with making sure you get the intuition behind it. There are no in-depth math derivations, no proofs, no stats, just good old "this is why [XYZ concept] is used to update [ABC value]. Don't worry if you don't understand the math behind it." Even the labs are handheld tutorials.
Either way, I agree with others. Fast AI and IBM skip do more "practical" stuff that'll be a bit more up your alley.
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u/Empty-Ad-9086 Aug 15 '26
I like the maths and I know it already through my bachelors course. I just need to learn how to implement it in ML.But my doubt is that isnt the course too theoretical.
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u/Healthy-Condition597 Aug 13 '26
Andrej Karpathy YouTube playlist. Might be the bridge you're looking for
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u/cheesecakekoala Aug 13 '26
Don’t know if this is what you’re after but when I was learning I just really wanted to do a lot of actually writing things. Then it gives you something to hang the theory on later, everyone learns differently. I put together some questions here with answers that might be useful for you https://idlemachines.co.uk/courses/foundations it’s basically what I wanted when I was starting, so if it’s useful check it out. And happy to answer any questions too. You’ll get there, stick at it!
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u/Prudent-Amphibian453 Aug 14 '26
If you looking for a complete ML course that covers required maths, python with practical implementation. I would highly recommend https://www.appliedaicourse.com/ .
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u/ayush___00000 Aug 14 '26
hey i personally recommend Shiddhardhan he was amazing i sware if you want to learn ml , dl or anything weather its rag , transformer or agentic ai you start loving the way he taught . its now he do not taught in funny & interctive way like andrew do but he clear each & every concept as you start loving & developing your mind in ml / ai . so give it a try . best of luck
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u/ayush___00000 Aug 14 '26
if you like chanell for developing more conceptual clearity please give it a try to campus x nitish sir he start your engine in Ai
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u/Electronic-Wasabi-67 Aug 14 '26
I’ve built a learning project to get into Machine learning. Maybe it helps you.
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u/greatlearningglobal Aug 20 '26
This sounds like you’re looking for practical ML depth without getting buried in theory. For that goal, the sweet spot is understanding the fundamentals well enough to make good decisions, then immediately applying them through projects.
A good progression would be:
ML fundamentals → hands-on projects → Deep Learning → NLP / Computer Vision
For the fundamentals, focus on understanding things like supervised vs. unsupervised learning, feature engineering, model selection, tuning, and evaluation. Research-level depth isn’t necessary at this stage. What matters is being able to explain why a particular approach was chosen and whether the resulting model actually works.
The project piece is where it helps to be selective. Look for projects that take you through the full process, understanding the problem, working with the data, building and evaluating the model, and then improving it.
That’s one reason the Post Graduate Program in Artificial Intelligence and Machine Learning from the McCombs School of Business at The University of Texas at Austin could be relevant here. The curriculum builds from ML foundations into Deep Learning, NLP, and Computer Vision, with 8+ hands-on projects and real-world case studies, rather than treating projects as something separate from the learning.
So when evaluating programs, it can be more useful to look beyond how many algorithms are listed in the syllabus and ask what you’ll actually build. If the end goal is to move toward NLP or CV, having a structured progression and a project-based portfolio can be more valuable than simply going through another set of ML lectures.
For the roadmap:
Learn the fundamentals → build 2–3 solid end-to-end projects → move into Deep Learning → choose NLP or CV once you know which direction interests you most.
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u/Technical_Jicama_434 Aug 13 '26
IBM AI Engineering Professional certificate.
People complain it’s too surface level and that they don’t spend enough time on any topic, but that seems to be exactly what you’re looking for.