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Building LLM Applications
4.8 (54)
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
Building LLM Applications
4.8 (54)
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
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
Interactive live sessions
Lifetime access to course materials
29 in-depth lessons
Direct access to instructor
6 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.
Building LLM Applications
Course syllabus
Week 1
Module 1: Introduction to NLP
Week 2
Module 2: Foundational Knowledge of Transformers & LLM System Design
Week 3
Module 3: Semantic Search
Week 4
Module 4: Creating a search engine from scratch
Week 5
Module 5 - The Generation Part of LLMs
Week 6
Module 6: Prompt-tuning, fine-tuning and local LLMS
Post-course
Demo Day
4.8 (54 ratings)
What students are saying
What people are saying
Victor Calderon
Tiffany Teasley
Darshil Modi
Tony Dupre
Nicole Lovold-Egar
Dan Kellen
Excited to have you here
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
Join an upcoming cohort
Building LLM Applications
Self-paced cohort
$750
Dates
Payment Deadline
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
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
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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Join an upcoming cohort
Building LLM Applications
Self-paced cohort
$750
Dates
Payment Deadline