LLM Zoomcamp: A Free Course on Real-Life Applications of LLMs

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Free Course

LLM Zoomcamp
Master Real-Life Applications of LLMs

A comprehensive 10-week course that teaches you how to build AI systems that answer questions about your knowledge base. Learn LLMs, RAG, vector search, evaluation, monitoring, and more.

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👥 73,000+ community
⏰ 10 weeks of content
⭐ Hands-on projects
OpenAI Qdrant Elasticsearch Arize Jupyter Notebook pandas scikit-learn

What You'll Learn

A comprehensive curriculum covering modern LLM applications

LLM Foundations

  • Introduction to LLMs
  • OpenAI API integration
  • Prompt engineering
  • Text generation
  • Question answering

Vector Search

  • Vector embeddings
  • Semantic search
  • Qdrant integration
  • Indexing strategies
  • Efficient retrieval

RAG & Agents

  • Retrieval-Augmented Generation
  • Knowledge base integration
  • Agentic functionality
  • Function calling
  • Advanced RAG patterns

Evaluation & Monitoring

  • Search evaluation
  • Online vs offline testing
  • LLM as a Judge
  • User feedback tracking
  • Performance monitoring

Best Practices

  • Hybrid search techniques
  • Document reranking
  • System optimization
  • Error handling
  • Production deployment

Project Development

  • End-to-end projects
  • Fitness assistant example
  • System architecture
  • Integration patterns
  • Deployment strategies

Prerequisites

Python Experience

Basic Python programming knowledge

  • Understanding of functions and classes
  • Working with APIs and HTTP requests
  • Basic data structures and JSON

Development Tools

Familiarity with basic development tools

  • Git version control
  • Command line operations
  • Virtual environments

LLM Knowledge

No prior LLM or ML experience required!

  • We'll teach LLMs from scratch
  • Step-by-step approach
  • Focus on practical applications

Meet the requirements? Start your journey into LLM engineering!

Join the Course

Course Syllabus

A comprehensive curriculum covering modern LLM applications

Module 1

Introduction to LLMs and RAG

  • Basics of LLMs
  • OpenAI API Integration
  • Text Search with Elasticsearch
  • RAG Fundamentals
  • Homework
Module 2

Vector Search

  • Vector Search & Embeddings
  • Indexing & Retrieval
  • Qdrant Integration
  • Efficient Data Storage
  • Homework
Bonus Module

Agents (Bonus)

  • Agentic Functionality
  • Function Calling
  • Advanced RAG Patterns
  • Homework
Module 3

Evaluation

  • Search Evaluation
  • Online vs Offline Testing
  • LLM as a Judge
  • Evaluation Metrics
  • Homework
Module 4

Monitoring

  • Online Evaluation
  • User Feedback Tracking
  • Monitoring Dashboards
  • Performance Analysis
  • Homework
Module 5

Best Practices

  • Hybrid Search
  • Document Reranking
  • System Optimization
  • Error Handling
  • Homework
Module 6

End-to-End Project

  • Fitness Assistant Project
  • System Architecture
  • Integration Patterns
  • Deployment Strategies
  • Project Review
Capstone Project

Build Your Own LLM Application

Apply everything you've learned to create a complete LLM-powered application.

  • End-to-end implementation
  • RAG system development
  • Evaluation and monitoring
  • Production deployment
  • Documentation and presentation

Ready to dive into LLM engineering?

Start Learning Today

How You'll Learn

Our comprehensive approach combines theory, practice, and community support

01

Weekly Assignments

Reinforce your learning with practical homework assignments that challenge and inspire.

02

Portfolio Project

Cap off your learning with an end-to-end project that showcases your new skills to potential employers.

03

Vibrant Community

Join DataTalks.Club on Slack to connect with peers, share insights, and get support when you need it.

Build Your ML Portfolio

Create a real-world project that showcases your skills to potential employers

Course Project

As an LLM engineer, personal projects are crucial for job interviews and demonstrating practical experience. You'll complete one comprehensive end-to-end RAG project with two submission attempts:

  • Project Requirements:
    • Select and prepare an interesting dataset
    • Build a knowledge base and ingestion pipeline
    • Implement retrieval and LLM integration
    • Evaluate RAG performance
    • Create a user interface
    • Implement monitoring and feedback collection
  • Technology Flexibility:
    • Choice of LLM (OpenAI, Ollama, Groq, etc.)
    • Various database options for knowledge base
    • Multiple interface frameworks (Streamlit, FastAPI, etc.)
    • Different monitoring solutions

Meet Your Instructors

Alexey Grigorev

Alexey Grigorev

Lead Instructor & Founder of DataTalks.Club

A seasoned software engineer and data scientist with over 10 years of engineering and 6 years of ML experience, Alexey specializes in bringing data science projects from early prototypes to production. His expertise spans problem identification, data collection, model creation, deployment, and maintenance.

Professional Background

  • Lead Data Scientist at OLX Group (Berlin) - Built large-scale ML systems processing 250+ images/second
  • Data Scientist at Simplaex - Developed ML infrastructure processing 3+ billion events daily
  • Data Scientist at Searchmetrics (Berlin)
  • Research positions at TU Berlin, Boston University Cognitive Neuroscience Lab

Publications & Achievements

  • Author of "Mastering Java for Data Science" (2017) and "TensorFlow Deep Learning Projects" (2018)
  • Kaggle Competition Master with multiple top-10 finishes, including 1st place in NIPS'17 Criteo Challenge
  • Published researcher with papers in SIGIR and WSDM
  • IT4BI Master's graduate (ULB, UFRT, TU Berlin)
Python & Java Expert ML Systems Design Kaggle Master Published Author Community Builder
DataTalks.Club
Timur Kamaliev

Timur Kamaliev

Senior Data Scientist at 1221 Systems

A seasoned data scientist with over 8 years of experience in industrial automation and AI systems. Currently working as a Senior Data Scientist at 1221 Systems, Timur brings practical expertise in implementing machine learning solutions and optimization systems at scale.

Professional Background

  • Senior Data Scientist at 1221 Systems
  • Data Scientist at Innostage
  • APC and Process Optimization Consultant at Emerson Automation Solutions
LinkedIn GitHub

Watch & Learn

Get to know our course better through these videos

Live Q&A

Get answers to the most common questions about the ML Zoomcamp

Launch Stream

Watch our course introduction and learn what to expect

Practical Workshops

Watch our recorded workshop sessions to get hands-on experience

Implement a Search Engine

Join Alexey Grigorev in this hands-on tutorial about building a search engine from scratch, focusing on text and vector search methods.

From RAG to Agents: Making Smart AI Assistants

Join Alexey Grigorev in this bonus module to learn how to build intelligent AI assistants using RAG and agent architectures.

From REST to Reasoning: Building Knowledge Graphs

Join Hiba Jamal to learn how to build knowledge graphs from REST API documentation using dlt and Cognee, modeling APIs as structured graphs of interconnected concepts.

3+

Hours of content

Free

Workshop recordings

24/7

Available on-demand

Want to participate in future live workshops?

Register for the Course

Learn in Public

Share your journey, build your presence, earn recognition

Why Learn in Public?

  • Earn Extra Points

    Get bonus points for sharing your progress, insights, and projects online

  • Build Your Portfolio

    Create valuable content that showcases your skills and knowledge

  • Increase Visibility

    Get noticed by social media algorithms and reach a broader audience

  • Network Growth

    Connect with individuals and organizations in the data science community

Extract from Shawn Wang's article about learning in public

Inspired by Shawn @swyx Wang's approach to learning in public

Everyone has something valuable to contribute, regardless of their expertise level

Start Your Learning Journey

Learn with Our Community

Join 73,000+ data practitioners in our thriving Slack community

Enhance your skills through peer learning
Connect with peers who share your passion
Get help and support when you need it
Build lasting friendships and connections
Course channel in our Slack community
100% FREE

Start Learning Today!

Choose your learning path: self-paced or join our current live cohort

Self-Paced Learning

$0 Start Immediately
  • Instant access to all course materials
  • Complete curriculum on GitHub
  • Access to all recorded sessions
  • Learn at your own pace
Access materials now

Join 10,000+ students learning LLMs!

Live Cohort (Current)

$0 Everything + Certificate
  • All self-paced features
  • Regular live office hours
  • Structured timeline with deadlines
  • Certificate upon completion
  • Peer learning & project reviews
Join current cohort

Join now - current cohort is live!

When is the next cohort?

If you miss the current cohort, the next one will start in May-June 2026. You can either join now or wait for the next cohort.

Frequently Asked Questions

Can I still join the course?

Yes! You can join the current cohort which is live now. To receive a certificate, you need to submit your project while we're still accepting submissions. If you miss the current cohort, the next one will start in May-June 2026.

Do I need a confirmation email after registering?

No, you don't need it. You're accepted automatically. You can start learning and submit your project (while submissions are open) without registering. Registration is mainly to gauge interest before the start date.

How do I access the course materials and live sessions?

  • Course materials are available on GitHub
  • Live sessions are streamed on YouTube
  • Questions during live sessions are submitted via Slido
  • Session links are posted in Slack & Telegram announcements

Can I get a certificate in self-paced mode?

No, certificates are only available when completing the course with a live cohort. This is because:

  • You need to peer-review 3 projects after submitting your own
  • Peer reviews can only be done during the active course period
  • Project submissions must be made while the submission form is open

What do I need to do to get certified?

To earn your certificate, you need to:

  • Submit your project (you have two attempts)
  • Complete peer reviews of 3 other projects
  • Submit while the course is active

Note: Weekly homework is recommended but not mandatory for certification.

When will the course be offered next?

The current cohort is live now. The next cohort will start in May-June 2026. You can either join the current cohort or wait for the next one.

Where can I find the lectures and videos?

All course content is available through:

  • DataTalks.Club's YouTube channel
  • Course GitHub repository
  • Bookmarked and pinned links in Slack

Sponsors & Supporters

A special thanks to our course sponsors for making this initiative possible!

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