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
• Duration: 28 weeks
• Level: Intermediate

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Where this track leads

• Junior AI Engineer — Build and deploy AI/ML systems in production
• 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

1. Python & Mathematics for ML4 chapters · 10 hours a week · 1 checkpoint
2. Machine Learning Fundamentals4 chapters · 12 hours a week · 1 checkpoint
3. ML Engineering & Deployment3 chapters · 8 hours a week · 1 checkpoint