Learn Practical Data Science, Programming, and Machine Learning. 25% Off for a Limited Time.
Join our Data Science Bootcamp
Join our Data Science Bootcamp
NEW
. 5 DAYS . 40 HOURS . IN-PERSON / ONLINE
10% OFF
4.95 · 640+ reviews
Includes:
INSTRUCTORS AND GUEST SPEAKERS
COURSE OVERVIEW
In collaboration with
Anyone interested in getting a headstart by getting a hands-on experience with building LLM applications.
Data professionals who want to supercharge their data skills using cutting-edge generative AI tools and techniques.
Product leaders at enterprises or startups seeking to leverage LLMs to enhance their products, processes and services.
A comprehensive introduction to the fundamentals of generative AI, foundation models and Large language models
An in-depth understanding of various LLM-powered application architectures and their relative tradeoffs
Hands-on experience with vector databases and vector embeddings
Practical experience with writing effective prompts for your LLM applications
Practical experience with orchestration frameworks like LangChain and Llama Index
Learn how to deploy your LLM applications using Azure and Hugging Face cloud
Practical experience with fine-tuning, parameter efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches
A custom LLM application created on selected datasets
10% OFF
4.95 · 640+ reviews
Includes:
Earn a Large Language Models certificate in association with the University of New Mexico Continuing Education, verifying your skills. Step into the market with a proven and trusted skillset.
In this module we will understand the common use cases of large language models and fundamental building blocks of such applications. Learners will be introduced to the following topics at a very high level without going into the technical details:
In this module, we will explore the primary challenges and risks associated with adopting generative AI technologies. Learners will be introduced to the following topics at a very high level without going into the technical details:
In this module, we will be reviewing how embeddings have evolved from the simplest one-hot encoding approach to more recent semantic embeddings approaches. The module will go over the following topics:
Dive into the world of large language models, discovering the potent mix of text embeddings, attention mechanisms, and the game-changing transformer model architecture.
Learn about efficient vector storage and retrieval with vector database, indexing techniques, retrieval methods, and hands-on exercises.
Understand how semantic search overcomes the fundamental limitation in lexical search i.e. lack of semantics . Learn how to use embeddings and similarity in order to build a semantic search model.