# AI & Machine Learning

From the math foundations (linear algebra, statistics, calculus) through classical ML (regression, classification, clustering) to deep learning and production deployment. End state: junior AI engineer or MLOps engineer.

- **Field:** [AI & Machine Learning](/en/fields/ai-ml)
- **Duration:** 28 weeks
- **Level:** Intermediate

::cta https://app.artimend.com/tracks/ai-machine-learning Start this track

## Where this track leads

- [Junior AI Engineer](/en/careers/junior-ai-engineer) — Build and deploy AI/ML systems in production
- [Junior MLOps Engineer](/en/careers/junior-mlops-engineer) — Manage ML model lifecycle, deployment, and monitoring

## What you will learn

- Implement core ML algorithms from scratch and with scikit-learn.
- Build, train, and evaluate deep neural networks with PyTorch / TensorFlow.
- Ship a model from notebook to a served API with monitoring.

## The learning paths in this track

::route
1. [Python & Mathematics for ML](/en/paths/python-mathematics-for-ml) — 4 chapters · 10 hours a week · 1 checkpoint
2. [Machine Learning Fundamentals](/en/paths/machine-learning-fundamentals) — 4 chapters · 12 hours a week · 1 checkpoint
3. [ML Engineering & Deployment](/en/paths/ml-engineering-deployment) — 3 chapters · 8 hours a week · 1 checkpoint
::