AI Engineer Roadmap (Free Resources)
AI Engineering is one of the fastest growing and highest impact roles in tech today. An AI Engineer builds intelligent systems using machine learning deep learning and modern AI models and then deploys them into real world applications. This roadmap is designed for beginners and intermediate learners who want a clear step by step path to become an AI Engineer using only free and high quality resources.
This roadmap is practical project driven and aligned with current industry expectations. You can follow it sequentially or jump to specific sections based on your background.
Who is this roadmap for
- Students and freshers exploring AI as a career
- Software developers transitioning into AI
- Data analysts moving toward machine learning
- Self learners looking for a structured AI path
Prerequisites mindset
You do not need to be a math genius or PhD to start. What you need is consistency curiosity and hands on practice. AI is learned by building not just watching videos.
Phase 1 Programming Foundations
1. Python for AI
Python is the backbone of AI and ML development.
What to learn
- Python syntax and data types
- Functions and OOP basics
- File handling and modules
- Virtual environments
Free resources
- Python Full Course by freeCodeCamp https://www.youtube.com/watch?v=rfscVS0vtbw
- Automate the Boring Stuff with Python https://automatetheboringstuff.com
- Python Docs https://docs.python.org/3/
Practice
- Build small scripts
- Solve problems on HackerRank Python section
- Python Interview Questions
Phase 2 Math for AI
You do not need deep theoretical math but you must understand concepts intuitively.
2. Linear Algebra
- Vectors and matrices
- Matrix multiplication
- Eigen values intuition
Resources
- Essence of Linear Algebra by 3Blue1Brown https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
3. Probability and Statistics
- Mean median variance
- Probability distributions
- Bayes theorem
Resources
- Khan Academy Statistics https://www.khanacademy.org/math/statistics-probability
4. Calculus basics
- Derivatives intuition
- Gradient descent idea
Resources
- Khan Academy Calculus https://www.khanacademy.org/math/calculus-1
Phase 3 Data Handling and Analysis
5. Data Analysis with Python
AI models are useless without data.
Tools
- NumPy
- Pandas
- Matplotlib
- Seaborn
Resources
- Data Analysis with Python freeCodeCamp https://www.youtube.com/watch?v=r-uOLxNrNk8
- Pandas Documentation https://pandas.pydata.org/docs/
Practice
- Analyze datasets from Kaggle
- Clean messy CSV files
Phase 4 Machine Learning Fundamentals
6. Core Machine Learning Concepts
What to learn
- Supervised vs Unsupervised learning
- Regression and classification
- Overfitting and underfitting
- Bias variance tradeoff
Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- KNN
- Naive Bayes
Resources
- Machine Learning roadmap
- Machine Learning by Andrew Ng https://www.coursera.org/learn/machine-learning
- StatQuest ML playlist https://www.youtube.com/c/joshstarmer
Practice
- Build ML models using scikit learn
- Kaggle beginner competitions
Phase 5 Deep Learning
7. Neural Networks
Concepts
- Perceptron
- Activation functions
- Backpropagation
- Loss functions
Frameworks
- TensorFlow
- PyTorch
Resources
- Deep Learning Specialization Andrew Ng https://www.coursera.org/specializations/deep-learning
- PyTorch Official Tutorials https://pytorch.org/tutorials/
- TensorFlow Tutorials https://www.tensorflow.org/tutorials
Practice
- Build a neural network from scratch
- Image classifier on MNIST
Phase 6 Specialized AI Domains
8. Computer Vision
Concepts
- CNNs
- Image classification
- Object detection
Resources
- CS231n Stanford https://cs231n.stanford.edu/
- freeCodeCamp Computer Vision https://www.youtube.com/watch?v=01sAkU_NvOY
9. Natural Language Processing
Concepts
- Tokenization
- Word embeddings
- Transformers
Resources
- NLP Course by freeCodeCamp https://www.youtube.com/watch?v=fOvTtapxa9c
- Hugging Face Course https://huggingface.co/learn
Phase 7 Modern AI and LLMs
10 Large Language Models
What to learn
- Transformers architecture
- Prompt engineering
- Fine tuning basics
- RAG pipelines
Resources
- Hugging Face Transformers Docs https://huggingface.co/docs
- OpenAI Cookbook https://github.com/openai/openai-cookbook
- LangChain Docs https://python.langchain.com
Practice
- Build a chatbot
- Create a document QnA system
Phase 8 MLOps and Deployment
11 Model Deployment
Skills
- FastAPI
- Docker basics
- Model versioning
- Monitoring
Resources
- FastAPI Docs https://fastapi.tiangolo.com
- Docker for Beginners https://www.youtube.com/watch?v=fqMOX6JJhGo
- MLflow Docs https://mlflow.org/docs/latest/index.html
Practice
- Deploy an ML model as API
- Host on cloud free tiers
Phase 9 Projects Portfolio
You should have at least 5 strong projects.
Project ideas
- Spam email classifier
- Resume screening AI
- Face recognition system
- AI chatbot using LLMs
- Recommendation system
Host code on GitHub and write clear README files.
Phase 10 Interview Preparation
Topics
- ML theory questions
- Python coding
- Model evaluation
- Case studies
Resources
- ML Interview Questions https://github.com/chiphuyen/machine-learning-systems-design
- LeetCode Python practice
Certifications optional
- Google Machine Learning Crash Course
- AWS Machine Learning Foundations
Final Advice
AI Engineering is a long term game. Focus on fundamentals build projects regularly and stay updated with the ecosystem. Do not chase tools blindly understand concepts first.
If you follow this roadmap with consistency you can confidently apply for AI Engineer and Machine Learning Engineer roles.
Bookmark this roadmap and revisit it every few months as you grow.
Join Telegram group for more resources & discussions!