Join the early access list for the next agentic AI coding bootcamp
Learning Path

AI & Machine Learning Programs

Go from data to deployed ML solutions

Learn how to build intelligent systems that solve real problems. This pathway blends classical machine learning with modern LLM development and production-grade deployment.

  • Duration: 10-14 weeks
  • Format: Live online + async
  • Commitment: 8-10 hrs/week
  • Level: Intermediate
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AI & Machine Learning Programs
OVERVIEW

What you can expect

You will learn how to work with data, train and evaluate models, and deploy intelligent features into real products. Expect hands-on labs, real datasets, and end-to-end ML workflows.

Model to production

Go beyond notebooks to robust ML services.

LLM application design

Build retrieval, tool-using, and agentic workflows.

Responsible AI

Learn evaluation, bias checks, and safety practices.

CURRICULUM

Courses in this pathway

Structured modules designed to build your skills in the right order.

Data & ML Foundations

Work with real datasets and build your first predictive models.

What you build
  • Data pipeline
  • Baseline model
Skills & tools
  • Feature engineering
  • Model evaluation
  • Metrics
  • Python
Applied Machine Learning

Train, tune, and validate models for real use cases.

What you build
  • Model experiments
  • Evaluation report
Skills & tools
  • Hyperparameter tuning
  • Model selection
  • Explainability
  • XGBoost
LLM Products & Retrieval

Design LLM features using retrieval and tool orchestration.

What you build
  • RAG system
  • LLM-powered assistant
Skills & tools
  • Prompting
  • RAG design
  • Tool integration
  • OpenAI API
MLOps & Deployment

Ship models with monitoring, testing, and safe rollout.

What you build
  • Model API
  • Monitoring dashboard
Skills & tools
  • Model serving
  • Monitoring
  • Rollouts
  • Docker
WHY THIS PATHWAY WORKS

Built for real-world delivery

Every module is designed to mirror production expectations.

End-to-end practice

Cover the full lifecycle from data to deployment.

Real datasets

Work with the kinds of data teams actually use.

Production readiness

Learn reliability, testing, and monitoring.

KEY TAKEAWAYS

What you will walk away with

ML project portfolio

Demonstrate applied ML and LLM skills.

Deployment skills

Ship models safely and monitor performance.

Responsible AI practices

Evaluate and mitigate risk in ML systems.

Join the early access list to get priority updates the moment we go live.

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FAQ

Questions, answered

A basic Python foundation is recommended.

Yes. We build RAG and tool-using LLM apps.

Yes. Deployment and monitoring are core modules.