Brought to you in collaboration

with Activeloop, Towards AI, & Intel Disruptor Initiative

Introducting Gen AI 360

Foundational Model Certification

Activeloop, Towards AI, and Intel Disruptor Initiative collaborate to bring Foundational Model Certification to tomorrow’s Gen AI professionals, executives and enthusiasts. The Foundational Model Certification is your essential gateway to mastering Large Language Models (LLMs) - from training to putting them in production. This cutting-edge three-course series designed to equip you with the knowledge and skills to train, fine-tune, and incorporate LLMs into AI products at your organization. In the first installment, jam-packed with 50+ lessons & 10+ practical projects, you will learn how to leverage LangChain, a robust framework for building applications with LLMs, and explore Deep Lake, a groundbreaking vector database for all AI data.

130+ 5-star Course Reviews

Course takers on the LangChain & Vector Databases in Production course

Why Should You Take This Course?

  • Master LLM & Vector Database Fundamentals

    Dive into the LLM world, mastering its fundamentals & theory. Utilize our top-tier tips for seamless production use, integrating APIs, & advanced prompt engineering. Grasp vector databases the key to preventing LLM hallucination & boosting information retrieval.

  • Build Production-Grade LLM Applications with LangChain

    Move beyond the shiny demos you see on the social media & build applications that matter - from building automated sales & customer support agents to building recommendation engines. Utilize the full power of LangChain with chains and agents & Deep Lake.

  • Master the only Multi-Modal Vector Database

    Deep Lake is the vector database for all AI data - whether this is text, images, videos, multiple embeddings to the same data, etc. Learn how to use Deep Lake to build an ultimate data moat at your organization.

Course Curriculum

    1. Introduction to the Course

    2. Course Introduction: Things You Should Know Before You Start

    3. Introduction to Course Modules

    4. Course Logistics

    1. LangChain 101: from Zero to Hero

    2. Let's Get to Know Each Other

    1. Introduction to LLMs and LangChain

    2. Quick Intro to Large Language Models

    3. Understanding Tokens

    4. Building Applications Powered by LLMs with LangChain

    5. Exploring the World of Language Models: LLMs vs Chat Models

    6. Exploring Conversational Capabilities with GPT-4 and ChatGPT

    7. Build a News Articles Summarizer

    8. Using the Open-Source GPT4All Model Locally

    9. What other models can we use? Popular LLM models compared

    10. LLMs & LangChain Module Quiz

    1. Intro to Prompting module

    2. Intro to Prompt Engineering: Tips and Tricks

    3. Using Prompt Templates

    4. Getting the Best of Few Shot Prompts and Example Selectors

    5. Managing Outputs with Output Parses

    6. Improving our News Articles Summarizer

    7. Creating Knowledge Graphs from Textual Data: Unveiling Hidden Connections

    8. Learning How to Prompt Module Quiz

    1. Intro to Indexes and Retrievers

    2. Exploring the Role of Langchain's Indexes and Retrievers

    3. Streamlined Data Ingestion: Text, PyPDF, Selenium URL Loaders, and Google Drive Sync

    4. What are Text Splitters and Why They are Useful

    5. Exploring the World of Embeddings

    6. Build a Customer Support Question Answering Chatbot

    7. Conversation Intelligence: Gong.io Open-Source Alternative AI Sales Assistant

    8. FableForge: Creating Picture Books with OpenAI and Deep Lake

    9. Saving 80% with Deep Lake HNSW Index: How to Rapidly Query 35M Vectors

    10. Keeping Knowledge Organized with Indexes Module Quiz

    1. Introduction to Chains

    2. Chains and Why They are Used

    3. Create a YouTube Video Summarizer Using Whisper and LangChain

    4. Creating a Voice Assistant for your Knowledge Base

    5. LangChain & GPT-4 for Code Understanding: Twitter Algorithm

    6. 3 Ways to Build a Recommendation Engine for Songs with LangChain

    7. Video Lesson: How We Built a Song Recommendation Engine with LangChain

    8. Guarding Against Undesirable Outputs with the Self-Critique Chain

    9. Combining Components Together with Chains Module Quiz

Course Highlights

  • Free
  • 68 lessons
  • Learn how to use the only multi-modal Vector DB
  • 10+ practical projects like building LLM-powered sales & customer support agents
  • 40 hours of learning content

Industry Leaders on the Course

Course Prerequisites

We've designed the course in a way that would be valuable for non-technical executives as well. To make the most out of it, however, you do need to be technical as it involves a lot of hands-on projects.

  • Intermediate Python Knowledge

  • Basic Knowledge of Jupyter Notebooks

  • Basic Knowledge of GitHub

More LangChain Course Takers' Reviews

Build LLM-powered apps, with LangChain & Deep Lake

Frequently Asked Questions

  • Is the course free?

    The course is absolutely free to take. While you do need an OpenAI account (you'd need a new account to redeem OpenAI credits) or a paid OpenAI account, we estimate the OpenAI API costs to be around $3. However, you can also use open-source LLMs across the course - which we cover in a related module.

  • Is the course up to date?

    Yes, we periodically update the course, so it is up to date. Please stick to recommended versions of the packages within the course for best experience.

  • Will I get a certificate upon completion?

    Yes, you will get Gen AI 360 Certified upon completion.

  • How long does the course take to complete?

    Our quickest learners have completed the course as quickly as in 25 hours, but the average course completion time is 40+ hours of learning.