Welcome to first LangChain course
This comprehensive course is designed to teach you how to use the LangChain library for building LLM-powered applications.
This course will equip you with the skills and knowledge necessary to develop LLM solutions for a wide range of topics.
The course starts with Introduction to Generative AI, which includes What is Generative AI, What are Discriminative Model and Generative Models and the Training Process of Generative Models.
In the Section 2, Introduction to Large Language Models is presented, which includes What are Large Language Models, Large Language Models Architecture, Difference between Traditional Machine Learning Models and Large Language Models, Use cases of Large Language Models, the lecture also covers, prompt design. Along with this, it also covers Zero Shot Learning and Few Shot Learning.
The Section 3 covers LangChain, What is LangChain, this section also covers OpenAI Large Language Models GPT 3.5, GPT 4, the limitations of these Large Language Models and how does LangChain overcomes these limitations.
The Section 4 covers LangChain, Large Language Models, Prompt Templates, Simple Sequential Chain, Sequential Chain, Agents & Memory.
In the Section 5, a Streamlit Application with LangChain and OpenAI for Instant Book Insights is been build.
The topics covered in this course include:
Generative AI
Large Language Models
LangChain
Prompts, PromptTemplates
Chains: SequentialChain, LLMChain
Agents
Memory
Streamlit (for UI)
Along with lifetime access to the course, you'll get:
Dedicated 1 on 1 troubleshooting support with me
No extra cost for continuous updates and improvements to the course
A Step By Step Approach To Recovering The Most Critical SQL Server Databases
Learn Android App Development with Android 8 Oreo by building real apps . Migrating existing app to Android Oreo ,Nougat
All you need to know to start freelancing with zero stress+ I personally will review your profile and a cover letter!
Teacher Ibrahim teaches you how to write with confidence
Machine Learning | Computer Vision Engineer with over 3 years of experience working in AI product development. I have worked on state of the art Object Detection and Tracking algorithms including YOLOv8, YOLOv7, YOLOR, YOLOv4 in Tracking I have reviewed and implemented, SORT, DeepSORT and ByteTrack in different projects. I also have an hands on experience on Classical and Traditional Machine Learning Algorithms. I have also reviewed and implemented state of the art Deep Learning Algorithms in different project which include VGG16, ResNet50, GoogLeNet, MobileNet. I have expertise in Natural Language and Time Series Forecasting as well and done plenty of project for different clients as well. I have created a real time Stock Price Forecasting Web App using state of the art Time Series Forecasting Model. I have done deployment on AWS servers, Google Cloud and Heroku as well.
