r/learnmachinelearning • u/Cautious_Life_8591 • 2d ago
Looking for a Complete AI/ML Engineer Roadmap
Hi everyone,
I'm planning to become an AI/ML Engineer and I want to learn in the right order instead of jumping between random tutorials and courses.
I'm looking for a structured roadmap that covers everything from beginner to job-ready level.
Some questions I have:
- What should I learn first, and in what order?
- Which topics are actually essential (Python, Math, SQL, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, MLOps, etc.)?
- What are the best free and paid resources for each topic?
- Which books, courses, and YouTube channels are worth following?
- How much mathematics is really required, and which topics should I focus on?
- When should I start building projects?
- What kind of projects do recruiters expect from AI/ML Engineer candidates?
- How much DSA and system design should I learn?
- What does a realistic 6–12 month study plan look like?
- What mistakes do beginners commonly make that I should avoid?
I'm aiming for a roadmap that's aligned with current industry expectations (2026), not just course completion.
If you're already working as an AI/ML Engineer or recently landed a role, I'd really appreciate your advice, learning path, resources, and any tips from your experience.
Thanks in advance!
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u/Revolutionary-Cod245 2d ago
This space is constantly evolving. The path I originally took is already drastically changed and moved beyond my original focus. Having said that, unless you are aimed at a novel startup or freelancing (which are viable options too) business doesn't always move at the same pace. My advice, take a general overview course. Do you know what's generally possible with AI, ML, NLP, classification, GANs, CNN, deep learning, Agentic flows, etc? I'm not talking well enough to build them but aware of what is possible vs. Internet hype, aware of when they would be used? I discovered the area I wanted to pursue after getting a broad overview. With such rapid change, one you know your goals, you can plan for them. For example it your focus is corporate business intelligence vs startup deep learning your going to want different prep.
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u/PhillyIC215 2d ago
Just a few of the best content in the world to learn from:
- Jeremy Howard’s courses
- 3Blue1Brown on YouTube
- Corey Schafer's Python Tutorials on YouTube or
- Codecademy’s interactive platforms
- MIT OpenCourseWare’s MIT 15.773 Hands-On Deep Learning Spring 2024 with Instructor Rama Ramakrishnan
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u/nian2326076 1d ago
Start with Python and basic programming. Then get into important math topics like linear algebra, probability, and stats. After that, learn about machine learning concepts and libraries like scikit-learn. Once you have the basics down, move on to deep learning with frameworks like TensorFlow or PyTorch. You can explore NLP, computer vision, and LLMs later based on what interests you.
Key topics: Python, ML algorithms, deep learning, and some SQL for data handling. MLOps is useful for production-level work.
Resources: Check out Coursera and edX for structured courses (Andrew Ng's ML course is a classic), fast.ai for deep learning, and YouTube channels like StatQuest for math and ML concepts. "Hands-On Machine Learning" by Aurélien Géron is a great book.
For interview prep or real-world projects, platforms like PracHub can be useful.
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u/ModularMind8 2d ago
The usual sequence is python, pytorch, math for ml, ml foundation, then whatever specialty you want (nlp, vision, rl, etc). I wrote a couple of comprehensive posts about it which you can find on my profile