Updated June 2026
Machine Learning Engineer
Learning Path
From Python foundations to production ML systems — a structured, free path to becoming an ML engineer, backed by real open source tools.
6 learning stages
10+ free courses
9–12 mo part-time pace
01 —
Python & Math Foundations
4–6 weeksThe language and mathematics every ML engineer needs.
What you'll learn
- Python (NumPy, Pandas, Matplotlib)
- Linear algebra: vectors, matrices, eigenvalues
- Probability and Bayes theorem
- Calculus: gradients and optimization basics
02 —
Classical Machine Learning
8–10 weeksThe algorithms every ML interview will test you on.
What you'll learn
- Linear & logistic regression
- Decision trees, random forests, gradient boosting
- SVMs and k-nearest neighbors
- Cross-validation, regularization, feature selection
03 —
Deep Learning
10–14 weeksNeural networks, from perceptrons to transformers.
What you'll learn
- Feedforward networks and backpropagation
- CNNs for computer vision
- RNNs, LSTMs, and GRUs for sequences
- Transformers and self-attention
04 —
Specialization Track
6–8 weeksPick a domain or go broad with LLMs.
What you'll learn
- NLP and large language models
- Computer vision and object detection
- Time-series forecasting
- Reinforcement learning basics
05 —
MLOps & Production Systems
6–8 weeksThe skills that separate ML engineers from data scientists.
What you'll learn
- Experiment tracking with MLflow
- CI/CD pipelines for ML (GitHub Actions)
- Model serving with FastAPI + Docker
- Monitoring: data drift and performance degradation
- Model registries and deployment workflows
06 —
Interview Preparation
4–6 weeksLand the role — top ML interview topics with practice.
What you'll learn
- ML algorithm internals (the "why" behind each)
- System design for ML (feature stores, pipelines)
- Statistics and probability interview questions
- Coding challenges: data structures for ML
Start your ML engineering journey
Every course is free. Every concept is backed by open source code you can run today.