AI Engineer Roadmap (100% Free Video + Docs Resources)

AI Engineer Roadmap 2026 — learn AI completely free.
No paid courses. Only curated 100% FREE videos and official docs to help you become a job-ready AI Engineer step-by-step.

The Roadmap Overview

AI Engineering brings together:

math → programming → data → ML → deep learning → MLOps → specialization → ethics

• Phase 1: Foundations
• Phase 2: Data Handling
• Phase 3: Machine Learning
• Phase 4: Deep Learning
• Phase 5: Frameworks & Libraries
• Phase 6: MLOps & Deployment
• Phase 7: Specializations
• Phase 8: AI Ethics

Phase 1: Foundations

Everything in AI sits on math + Python + problem solving.

Linear Algebra

Start with vectors and matrices. These power neural networks and embeddings.

Video Resources

Course

Docs & Reading

• https://www.infosysbpm.com/offerings/business-transformation-services/insights/documents/linear-algebra-in-ai.pdf
• https://mml-book.github.io/book/mml-book.pdf
• https://course.ccs.neu.edu/ds4420sp20/readings/mml-book.pdf

Calculus

Focus on derivatives + gradients. They explain how models learn.

Video Resources

Calculus Playlist

Docs

• https://mml-book.github.io/book/mml-book.pdf
• https://d3bxy9euw4e147.cloudfront.net/oscms-prodcms/media/documents/CalculusVolume1-OP.pdf
• https://ocw.mit.edu/courses/res-18-001-calculus-fall-2023/pages/textbook/

Probability & Statistics

Understand uncertainty, distributions, averages, variance, inference.

Video Resources

Course 1	Course 2

Docs

• https://www.probabilitycourse.com/
• https://korivernon.com/documents/MathematicalStatisticsandDataAnalysis3ed.pdf

Python Programming

Learn syntax, loops, lists, functions — before AI libraries.

Video Resources

Python Crash Course	Python Full Course

Docs

• https://python.codersnote.com/
• https://docs.python.org/3/
• https://realpython.com/

Data Structures & Algorithms

Efficient code = faster AI pipelines.

Video Resources

Full Course

Git & Version Control

Track experiments, collaborate safely, revert mistakes.

Video Resources

Git Course	GitHub

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Phase 2: Data Handling

AI depends on clean, structured, understandable data.

NumPy

Work with arrays — faster than loops.

Video Resources

Playlist	Guide

Pandas

Clean data, handle missing values, merge datasets.

Video Resources

Pandas

Data Visualization

Use graphs to see trends and problems.

Video

Visualization

SQL

Query databases to feed AI models.

Videos

SQL Course	1-Month Guide

Phase 3: Machine Learning

Build models that learn from data and make predictions.

Machine Learning Basics

Learn what ML really means — datasets, features, labels, training, testing, and avoiding overfitting.

Video Resources

Beginner Course	Stanford CS229

Docs & Reading

• https://github.com/dair-ai/Mathematics-for-ML
• https://www.jonkrohn.com/resources
• https://www.reddit.com/r/learnmachinelearning/comments/adwft2/all_the_math_you_might_need_for_machine_learning/

Supervised Learning

Train models when you already know the answers (labels) — like house prices, spam vs not spam, sentiment, etc.

Video Resources

Full Course	Lecture

Docs

• https://www.statlearning.com/
• https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

Unsupervised Learning

Discover hidden groups and structures — clustering, dimensionality reduction, anomaly detection.

Video

Unsupervised

Docs

• http://karpathy.github.io/2015/05/21/rnn-effectiveness/
• https://arxiv.org/abs/1810.03737
• https://towardsdatascience.com/clustering-algorithms-explained-7d5b6f4f0c0a

Model Evaluation & Metrics

Learn which metric to trust — accuracy is NOT always enough.

Video Resources

Evaluation Course	Beginner Tutorial

Docs

• https://neptune.ai/blog/ml-model-evaluation-and-selection
• https://pmc.ncbi.nlm.nih.gov/articles/PMC11420621/

Phase 4: Deep Learning

Teach machines to learn complex representations.

Intro to Deep Learning

Understand layers, activations, forward pass, and backpropagation.

Videos

MIT	Playlist

Docs

• https://www.deeplearningbook.org/
• https://d2l.ai/

Artificial Neural Networks (ANN)

Build and train simple neural networks from scratch.

Videos

Zero to Hero	Full Course

Docs

• http://neuralnetworksanddeeplearning.com/
• https://udlbook.github.io/udlbook/

Convolutional Neural Networks (CNN)

Best for images, vision tasks, and pattern recognition.

Videos

Beginners	MIT

Docs

• https://cs231n.github.io/

Recurrent Neural Networks (RNN)

Handle sequences — speech, text, time series.

Videos

MIT	Explained

LSTM

Improve RNNs to remember long-term patterns.

Videos

LSTM Course	Deep Dive

Transformers

The architecture behind GPT, BERT, Llama, and modern AI.

Videos

Karpathy	Transformers Playlist

Phase 5: Frameworks & Libraries

Turn theory into real projects.

Scikit-learn

Fast prototyping for classical ML.

Videos

Crash Course	Full Course

TensorFlow

Great for production-ready deep learning.

Videos

TensorFlow Course	TensorFlow + PyTorch

PyTorch

Flexible and popular for research + experiments.

Videos

Crash Course	Full Course

Keras

Beginner-friendly deep learning wrapper over TensorFlow.

Videos

Full Course	Playlist

Hugging Face

Use and fine-tune powerful pre-trained AI models.

Videos

Hugging Face Course	Learn in 1 Hour

Phase 6: MLOps & Deployment

Ship AI into real-world production systems.

Docker

Package models so they run the same everywhere.

Videos

AI + Docker	ML Docker

Kubernetes

Scale AI apps across multiple machines.

Videos

Getting Started	Kubernetes Course

MLflow

Track experiments, versions, and models.

Videos

Intro	Full Overview

CI/CD for ML

Automate testing and deployment of ML pipelines.

Videos

Talk	GitHub Actions

Deployment Platforms

Deploy models at scale on cloud platforms.

Videos

MLOps Full Course	Cloud MLOps

Phase 7: Specializations

Choose your path once foundations are solid.

NLP

Teach machines to understand and generate language.

Videos

Full Course	Playlist

Computer Vision

AI that sees and understands images and video.

Videos

Intro	Advanced

Reinforcement Learning

Train AI agents using rewards and penalties.

Videos

Full Course	MIT

Generative AI

Create new text, images, and content using AI.

Videos

Full Course	Developer Course

Phase 8: AI Ethics

Build AI systems responsibly.

AI Ethics & Responsible AI

Understand fairness, privacy, transparency and bias.

Videos

Full Course	Short Overview

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Contributing

Have an amazing free AI resource?

1. Fork this repo
2. Add your resource
3. Open a Pull Request

Support

If this roadmap helped — please ⭐ star the repo.

Helpful Links

• Interactive AI Engineer Roadmap: AI Engineer Roadmap
• AI Tutor Lyra: https://codersnote.com/ai-tutor