Building LLM Applications

4.8

(49 ratings)

·

5 Weeks

·

Cohort-based Course

Gain a thorough understanding of the world of Large Language Models with a deep understanding on how to build your own applications

Previously at

Google
company logo
University of Minnesota
Stanford University

Course overview

Build LLM Powered Applications, like a pro!

This course is amongst the top-rated technical courses and I am proud to say it's an extremely popular course So far I have taught this course to over 300 Professional.


The course is aimed to introduce you to Large Language Models in deeper detail on what is Transformer Architecture and how we utilize the Encoder and Decoder Models.


This is not a Langchain course but much more than that. We go into great depth to explain the building blocks of retrieval systems and how to utilize Open Source LLMs to build your own RAG-based architectures.


Learning Outcome:


- Gain a comprehensive understanding of the end-to-end LLM architecture


- Construct and deploy robust and effective models in real-world settings using Large

Language Models. 


- Learn to tackle practical machine learning problems and deliver results in production.



Additionally, students will gain a comprehensive understanding of the end-to-end machine learning pipeline, allowing them to construct and deploy robust and effective models in real-world settings using Large Language Models. Overall, students will emerge with greater confidence in their abilities to tackle practical machine learning problems and deliver results in production.


Please Note:

This is not a beginner course for non technical people, it requires knowledge of Python and some basic machine learning background.

Who is this course for

01

You are intrigued about LLMs and would like to build applications powered by LLMs

02

You are ready to deploy your own SOTA AI Models and like to see how they work

03

You want to go beyond Jupyter Notebook and develop batch or real-time prediction

What you’ll get out of this course

Collect and preprocess data for large language models


Train and fine-tune pre-trained large language models for specific tasks


Evaluate the performance of large language models and select appropriate metrics


Deploy large language models in real-world applications using APIs and Huggingface


Understand ethical considerations involved in working with large language models, such as avoiding bias and ensuring transparency

This course includes

7 interactive live sessions

Lifetime access to course materials

23 in-depth lessons

Direct access to instructor

7 projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules
  • Week 1

    Jul 7

    Week dates are set to instructor's time zone

    Events

    • Jul

      7

      Session 1

      Sun, Jul 7, 4:00 PM - 6:00 PM UTC

    Modules

    • Module 1: Introduction to NLP

  • Week 2

    Jul 8—Jul 14

    Week dates are set to instructor's time zone

    Events

    • Jul

      13

      Session 2

      Sat, Jul 13, 4:00 PM - 6:00 PM UTC

    • Jul

      14

      Session 2

      Sun, Jul 14, 4:00 PM - 6:00 PM UTC

    Modules

    • Free preview

      Module 2: Foundational Knowledge of Transformers & LLM System Design

  • Week 3

    Jul 15—Jul 21

    Week dates are set to instructor's time zone

    Events

    • Jul

      21

      Session 3

      Sun, Jul 21, 4:00 PM - 6:00 PM UTC

    Modules

    • Free preview

      Module 3: Semantic Search

  • Week 4

    Jul 22—Jul 28

    Week dates are set to instructor's time zone

    Events

    • Jul

      28

      Session 4

      Sun, Jul 28, 4:00 PM - 6:00 PM UTC

  • Week 5

    Jul 29—Aug 4

    Week dates are set to instructor's time zone

    Events

    • Aug

      4

      Session 5

      Sun, Aug 4, 4:00 PM - 6:00 PM UTC

  • Week 6

    Aug 5—Aug 10

    Week dates are set to instructor's time zone

    Nothing scheduled for this week.

  • Post-Course

    Events

    • Aug

      11

      Session 6

      Sun, Aug 11, 4:00 PM - 6:00 PM UTC

    Modules

    • Demo Day

    • Free preview

      Module 4: Creating a search engine from scratch

    • Module 5 - The Generation Part of LLMs

    • Module 6: Prompt-tuning, fine-tuning and local LLMS

4.8

(49 ratings)

What students are saying

What people are saying

        The course was great! It was full of great content, a very active cohort, and I feel I learned many methods for putting Large Language Models (LLM) into production. The course was very well-structured, starting with the basics of NLP and building up towards analyzing and describing documents using NLP techniques, all the way to deploying an API
Victor Calderon

Victor Calderon

Senior Machine Learning Engineer
        Amazing! I am leaving this course feeling empowered and equipped with the skills to leverage LLMs to build and deploy applications. I was able to implement the knowledge I learned immediately at work and with personal projects. Hamza is an awesome instructor. He is passionate about this topic and was able to simplify the concepts
Tiffany Teasley

Tiffany Teasley

Data Scientist & Career Coach at Data Sistah
        Prof Hamza Farooq is a far-sighted, application oriented AI Researcher and a great Professor. Working under him was full of learnings and practical knowledge
Darshil Modi

Darshil Modi

Cohort 1
        Hamza provided several excellent projects to learn from, showcasing quite a few ML practices and options in each. Learned a ton that I continually go back to!
Tony Dupre

Tony Dupre

Cohort 1
        Hamza's class was among my favorites of my Master's program! He makes the tools of machine learning accessible and his teaching skills are on point.
Nicole Lovold-Egar

Nicole Lovold-Egar

Beta Cohort
        Hamza was an excellent instructor. He was able to explain various machine learning techniques in ways that were easy to understand and apply them to real world problems
Dan Kellen

Dan Kellen

Beta Cohort

Excited to have you here

Hamza Farooq

Hamza Farooq

Founder | Ex-Google | Adjunct Professor Stanford & UCLA

I am a Founder by day and Professor by night. My work revolves in the realm of LLMs and Multi-Modal Systems.


My startup, traversaal.ai was built with one vision: provide scalable LLM Solutions for Startups and Enterprises, which can seamlessly integrate within the existing ecosystem, while being customizable and cost efficient.


This course is a cumulation of all my learnings and the courses I teach at other universities

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Join an upcoming cohort

Building LLM Applications

Cohort-2024-03

$750 USD

Dates

July 7—Aug 10, 2024

Payment Deadline

July 6, 2024
|

Bulk purchases

Course schedule

4-6 hours per week
  • Saturday: Module Teaching

    8am - 10:00am PST

    We will go through each module during this class

  • Weekly projects

    2-4 hours per week

    Students will spend time building projects with their team members or individually

Free resource

Building LLM Applications from Scratch

this course with a focus on production and LLMs is designed to equip students with practical skills necessary to build and deploy machine learning models in real-world settings. Be part of the first 20 people cohort. More in email link..

Join Waitlist!

Learning is better with cohorts

Learning is better with cohorts

Active hands-on learning

This course builds on live workshops and hands-on projects

Interactive and project-based

You’ll be interacting with other learners through breakout rooms and project teams

Learn with a cohort of peers

Join a community of like-minded people who want to learn and grow alongside you

Frequently Asked Questions

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A pattern of wavy dots
Join an upcoming cohort

Building LLM Applications

Cohort-2024-03

$750 USD

Dates

July 7—Aug 10, 2024

Payment Deadline

July 6, 2024
|

Bulk purchases

$750 USD

4.8

(49)

·

5 Weeks