All roadmaps
🤖

ML/AI

Machine Learning / AI Engineer

From math and Python to building, training, and deploying models — including modern deep learning and LLM applications.

PythonNumPyPyTorchscikit-learn
1

Math & Python foundations

Python for data (NumPy, pandas)

Essential

Linear algebra & calculus essentials

Recommended

Probability & statistics

Recommended
2

Classical machine learning

Supervised learning (regression, classification)

Essential

Model evaluation & validation

Essential

Feature engineering

Recommended
Build it

Train a classifier end-to-end

intermediate

Clean data, train, evaluate, and tune a scikit-learn model.

3

Deep learning

Neural network fundamentals

Recommended

A deep learning framework (PyTorch)

Recommended
4

LLMs & applied AI

Working with LLM APIs

Recommended

Retrieval-augmented generation (RAG)

Optional
5

MLOps & deployment

Serving models in production

Recommended

Experiment tracking & reproducibility

Optional

Land the job

Turn the skills you built into offers — resume, tailoring, applications, and interview prep.

Build a recruiter-ready resume

Essential

Use the builder to produce a clean, ATS-friendly resume from your career memory.

Go

Tailor your resume to each role

Recommended

Match keywords and highlight the experience each job actually asks for.

Go

Browse and apply to open roles

Essential

Find roles that match the skills on this path and apply with your tailored resume.

Go

Prep for interviews

Essential

Practice coding, system design, and behavioral rounds with AI-driven mock interviews.

Go