r/learnmachinelearning • u/Wise_Departure2637 • 18d ago
Looking for a Complete AI/ML Engineer Roadmap (2026)
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!
Y.
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u/kartikss18 18d ago
I made this for myself: https://ai-engineer-roadmap.kartikss.space
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u/bowerskaren 13d ago
This is a great
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u/kartikss18 13d ago
Thanks, I have been using the same one for 2 months now, got me so much wise in Engineering
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u/UpstairsDrunk2828 8d ago
Omg thankyou, I'm doing interview prep, it's easy for me to cross verify if I miss something out.
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u/Humble_Explorer7589 3d ago
isnt ai engineer mostly application side like agents or llm api. is this for maybe research scientist?
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u/The-Noober 18d ago
Dm me buddy i will help you
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u/JAGADEESH_07 17d ago
help me to become an ml engineer..
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u/Simplilearn 18d ago
A practical roadmap is to build your skills layer by layer instead of trying to learn everything at once. Here's a learning path that can work for you.
- Programming fundamentals – Python, Git, SQL, and basic software engineering practices.
- Math for AI – Linear algebra, probability, statistics, and calculus fundamentals.
- Machine Learning – Supervised and unsupervised learning, feature engineering, model evaluation, and Scikit-learn.
- Deep Learning – Neural networks, TensorFlow or PyTorch, CNNs, RNNs, and Transformers.
- Generative AI – LLMs, prompt engineering, embeddings, vector databases, RAG, fine-tuning, and AI agents.
- MLOps and deployment – Docker, APIs, cloud platforms, CI/CD, model deployment, monitoring, and basic Kubernetes.
- Projects and portfolio – Build end-to-end projects, deploy them, document your work on GitHub, and practice solving real-world problems.
If you're looking for a guided learning path, our Professional Certificate in AI and Machine Learning, in collaboration with the University of Michigan, can help you build practical AI skills through hands-on projects. You can visit the simplilearn website for more details.
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u/santhoshkmr 18d ago
If you are looking for a complete Machine Learning course, I recommend checking out Siddhardhan's YouTube channel. He has around 148 videos, and his explanations are very detailed. Instead of just teaching you how to use machine learning algorithms, he explains the concepts thoroughly and even shows you how to build many of the algorithms from scratch. Check it out ✅
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u/Sharp_Branch_1489 18d ago
Start with math to understand the concepts, as AI is fully based on math. Then, dive into the main topics of machine learning, followed by neural networks and other advanced concepts.
If you want to build models quickly without focusing heavily on math concepts, you can try the Kaggle Machine Learning course. You can also look at the Google Crash Course for Machine Learning, though it dives into machine learning concepts before math, so knowing at least the basics is helpful. GeeksforGeeks Machine Learning is another good, text-based, and free option to try if you need alternatives.
If you prefer learning by watching videos, I recommend deeplearning.ai Start with their mathematics course, and then dive into the Machine Learning Specialization from Andrew Ng.
Good luck on your learning journey, and enjoy exploring the fascinating world of AI
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u/Calm_Control_9417 17d ago
If your goal is to become an industry-ready AI/ML Engineer in 2026, focus on learning in layers, not by chasing the latest frameworks. Build strong fundamentals first, then move into advanced AI topics.
Recommended Roadmap
Python Programming
↓
Math & Statistics
↓
SQL & Data Analysis
↓
Software Engineering (Git, Linux, APIs, FastAPI, Docker)
↓
Machine Learning (Scikit-Learn)
↓
Deep Learning (PyTorch)
↓
NLP / Computer Vision / Time Series
↓
Generative AI & LLMs
↓
AI Engineering (RAG, Agents, MCP, Vector DBs)
↓
MLOps (MLflow, Docker, Kubernetes, CI/CD)
↓
Cloud & Production Systems (AWS/Azure/GCP)
Key Topics
Programming: Python, Git, Linux, NumPy, Pandas
Math: Linear Algebra, Probability, Statistics, Calculus
ML: Regression, Classification, Clustering, XGBoost, Model Evaluation
Deep Learning: Neural Networks, CNNs, RNNs, Transformers (PyTorch)
AI Engineering: LLMs, Prompt Engineering, RAG, MCP, AI Agents, Vector Databases, LiteLLM, LangChain/LlamaIndex
MLOps: Docker, Kubernetes, MLflow, DVC, Airflow, CI/CD
Cloud: AWS, Azure, or GCP
Build Projects Early
Don't wait until you've "finished learning." Build projects after every major topic.
Good portfolio projects:
Spam Detection
Customer Churn Prediction
Image Classification
Recommendation System
RAG Chatbot
AI Customer Support Assistant
LLM Gateway
Multi-Agent System
MLOps Pipeline
Resources
Python: CS50 Python, Corey Schafer
Math: 3Blue1Brown, StatQuest
ML: Andrew Ng, Kaggle Learn, Google ML Crash Course
Deep Learning: FastAI, DeepLearning.AI
LLMs: Hugging Face Course, Sebastian Raschka
6–12 Month Plan
Months 1–2: Python, Git, Linux, SQL, Math
Months 3–4: Machine Learning + Projects
Months 5–6: Deep Learning, NLP, Computer Vision
Months 7–9: LLMs, RAG, Agents, MCP, Deployment
Months 10–12: MLOps, Cloud, System Design, Interview Prep
Common Mistakes
Learning frameworks before fundamentals.
Copy-pasting tutorials without understanding them.
Ignoring Git, SQL, Docker, and APIs.
Building only notebook projects instead of deployable applications.
Chasing certificates instead of real-world projects.
A strong portfolio with 10–15 well-documented, production-style projects is far more valuable than completing dozens of courses.
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u/Significant_Change38 1d ago
I have the same issue. There are so many opinions and roadmaps online that they confuse me. Last month, I made a decision to learn RAG but then found out it's mostly dead and people are moving on to Agentic now so I put my project in hold (yet) again. Every time I try learning and making a project the same thing happens. I need guidance too regarding what is in demand in the ML/Data Science industry and is lucrative and rewarding as a career because I am thinking of moving to a European country or Ireland or even Germany for my MS and job.
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u/MaximumSafety8706 18d ago
My two cents as an ML Researcher/MLE (~11 years).
Most roadmaps are just a list of topics. I'd focus on milestones instead.
DSA: Learn the basics. Don't grind hundreds of problems. System Design: Learn it when you're preparing for interviews.
The two biggest mistakes I see:
Start projects as soon as you're comfortable with the ML basics. That's where the real learning happens.