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Data & AI

Build real applications on top of machine learning and LLMs.

Intermediate to Advanced 5–6 months 6 stages
Start learning
1
1

Math & Programming Foundations

Build the technical base every ML engineer needs.

Python Linear algebra Probability & statistics
2
2

Machine Learning Fundamentals

Understand how models learn from data and when they fail.

Supervised & unsupervised learning Model evaluation Feature engineering
Machine Learning A–Z: Hands-On Python & Real Projects
3
3

Deep Learning

Move from classical ML into modern deep learning.

Neural networks CNNs & RNNs Training at scale
4
4

Large Language Models

Build applications on top of today’s most capable models.

Prompt engineering Fine-tuning RAG pipelines
Generative AI Engineering with LLMs
5
5

MLOps & Deployment

Get models out of notebooks and into production.

Model serving Monitoring Cloud GPUs
AWS Cloud & DevOps Engineering
6
6

Get Hired

Demonstrate applied, production-ready ML skills.

ML portfolio Reading research papers Take-home case studies

More Data & AI roadmaps

Data AnalystTurn raw data into decisions stakeholders act on.
Intermediate to Advanced · 5–6 months

Work through all 6 stages of the AI / ML Engineer roadmap with courses built for exactly this path.

Browse courses for this pathView all roadmaps
  • Math & Programming Foundations
  • Machine Learning Fundamentals
  • Deep Learning