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COURSE WAS RE-RECORDED ON MID APRIL 2024- LangChain Version 0.1.16

Welcome to first LangChain Udemy course - Unleashing the Power of LLM. This comprehensive course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.

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COURSE WAS RE-RECORDED ON MID APRIL 2024- LangChain Version 0.1.16

Welcome to first LangChain Udemy course - Unleashing the Power of LLM. This comprehensive course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.

Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .In this course, you will embark on a journey from scratch to building a real-world LLM powered application using LangChain. We are going to do so by build 3 main applications:

  1. Ice Breaker- LangChain agent that given a name, searches in google to find Linkedin and twitter profiles, scrape the internet for information about a name you provide and generate a couple of personalized ice breakers to kick off a conversation with the person.

  2. Documentation Helper- Create chatbot over a python package documentation. (and over any other data you would like)

  3. A slim version of ChatGPT Code-Interpreter

The topics covered in this course include:

  • LangChain

  • History

  • LLMs: Few shots prompting, Chain of Thought, ReAct prompting

  • Chat Models

  • Prompts, PromptTemplates

  • Output Parsers

  • Chains: SequentialChain, LLMChain, RetrievalQA chain

  • Agents, Custom Agents, Python Agents, CSV Agents, Agent Routers

  • OpenAI Functions

  • Tools, Toolkits

  • Memory

  • Vectorstores (Pinecone, FAISS)

  • DocumentLoaders, TextSplitters

  • Streamlit (for UI)

  • LCEL

  • LangSmith

Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.This is not just a course, it's  also  a community. Along with lifetime access to the course, you'll get:

  1. Dedicated 1 on 1 troubleshooting support with me

  2. Github links with additional AI resources, FAQ, troubleshooting guides

  3. Access to an exclusive Discord community to connect with other learners (5000+ members)

  4. No extra cost for continuous updates and improvements to the course

This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.

  • The first project of the course (Ice-Breaker) requires usage of 3rd party APIs-ProxyURL, SerpAPI, Twitter API  which are generally paid services.All of those 3rd parties have a free tier we will use to create stub responses development and testing.

  • Enroll now

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    Good to know

    Know what's good
    , what to watch for
    , and possible dealbreakers
    Develops skills and knowledge in using LLM for real-world applications, which is highly relevant to industry
    Builds a strong foundation for beginners in using LLM
    Taught by instructors who are recognized for their work in LLM
    Includes hands-on labs and interactive materials
    Requires proficiency in Python, which may be a barrier for some learners
    Relies on third-party APIs which may require paid subscriptions

    Save this course

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    Activities

    Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LangChain- Develop LLM powered applications with LangChain with these activities:
    Review Python basics
    Ensure a solid understanding of Python before starting the course.
    Browse courses on Python
    Show steps
    • Review Python syntax and data structures
    • Practice writing simple Python programs
    Read "Lady in the Water" by Emily St. John Mandel
    Review the similarities and differences in communication styles and their outcomes between The Last Hotel and Station Eleven.
    Show steps
    • Read the book
    • Annotate important passages
    • Reflect on the book's themes
    Read "The Last Hotel" by Emily St. John Mandel
    Review the similarities and differences in communication styles and their outcomes between The Last Hotel and Station Eleven.
    Show steps
    • Read the book
    • Annotate important passages
    • Reflect on the book's themes
    Five other activities
    Expand to see all activities and additional details
    Show all eight activities
    Complete the official LangChain tutorial
    Gain hands-on experience and solidify your understanding of LangChain.
    Browse courses on LangChain
    Show steps
    • Install the LangChain library
    • Follow the official LangChain tutorial
    • Experiment with different LangChain features
    Solve Leetcode 3-Sum Problem
    Practice solving and debugging coding problems.
    Browse courses on Array Manipulation
    Show steps
    • Understand the problem statement
    • Implement a brute force solution
    • Implement an optimized solution
    • Debug and test your code
    Write a blog post about LLM applications
    Demonstrate your understanding of LLMs by writing about their applications.
    Show steps
    • Research LLM applications
    • Write the blog post
    • Publish and promote your blog post
    Build a chatbot to simulate a customer support conversation
    Demonstrate your understanding of NLP and machine learning by building a practical application.
    Browse courses on Chatbot Development
    Show steps
    • Design the chatbot's conversation flow
    • Train a language model
    • Deploy the chatbot
    Participate in a Kaggle competition
    Challenge yourself and showcase your skills by participating in a real-world competition.
    Browse courses on Data Science
    Show steps
    • Select a competition that aligns with your interests and skills
    • Build and submit your model
    • Analyze your results and learn from your experience

    Career center

    Learners who complete LangChain- Develop LLM powered applications with LangChain will develop knowledge and skills that may be useful to these careers:
    Computational Linguist
    Computational Linguists use large language models to analyze and understand human language. This course can help you create and use large language models for computational linguistic tasks.
    Natural Language Processing Engineer
    Natural Language Processing Engineers specialize in developing and applying LLMs. This course offers the perfect foundation for a smooth entry into the field.
    Artificial Intelligence Researcher
    Artificial Intelligence Researchers use LLMs to develop new and innovative AI technologies. This course on LangChain will help you learn the basics of LLMs and apply them to your research.
    Machine Learning Engineer
    Machine Learning Engineers use large language models to develop and deploy machine learning models. This course in LangChain can greatly help you master the fundamentals.
    Consultant
    Consultants use large language models to analyze data, identify trends, and develop solutions for clients. This course in LangChain can help you gain the skills you need to use LLMs in your consulting work.
    Business Analyst
    Business Analysts use large language models to analyze data, identify trends, and develop recommendations for businesses. This course in LangChain can help you learn how to use LLMs to improve your business analysis skills.
    Product Manager
    Product Managers often use large language models to gather customer feedback, analyze market trends, and develop new products. This course on LangChain can help you get started with LLMs and apply them to your work.
    Technical Writer
    Technical Writers use large language models to generate documentation, write code comments, and create training materials. This course on LangChain can help you learn how to use LLMs to improve your technical writing skills.
    Operations Manager
    Operations Managers use large language models to analyze data, identify inefficiencies, and develop solutions to improve operations. This course on LangChain may be useful to you in developing operational strategies.
    Sales Manager
    Sales Managers use large language models to analyze customer data, identify sales opportunities, and close deals. This course on LangChain may be useful to you in developing strategies and improving sales performance.
    Marketing Manager
    Marketing Managers use large language models to analyze customer data, create marketing campaigns, and track results. This course on LangChain may be useful to you in creating more sophisticated marketing campaigns.
    Customer Success Manager
    Customer Success Managers use large language models to analyze customer feedback, identify customer needs, and develop customer success strategies. This course on LangChain may be useful to you in developing customer success strategies.
    Educator
    Educators use large language models to create lesson plans, develop educational materials, and provide feedback to students. This course on LangChain may be useful for you in developing lesson plans and study materials.
    Software Engineer
    Software Engineers often use large language models in their work. This course on LangChain would help you get started in the domain.
    Data Scientist
    Data Scientists use large language models to analyze data, build models, and generate insights. Taking this course on LangChain can help you master the basics of LLMs and apply them to your work.

    Reading list

    We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LangChain- Develop LLM powered applications with LangChain.
    Provides a comprehensive overview of deep learning for NLP, covering topics such as word embeddings, recurrent neural networks, transformers, and attention mechanisms. It also includes practical examples and exercises to help readers apply their knowledge.
    Provides a comprehensive overview of AI for NLP, covering topics such as machine learning, deep learning, and neural networks. It also includes discussions of NLP applications such as text classification, question answering, and machine translation.
    Provides a comprehensive overview of machine learning for NLP, covering topics such as supervised learning, unsupervised learning, and deep learning. It also includes discussions of NLP applications such as text classification, question answering, and machine translation.
    Provides a comprehensive overview of machine learning for text, covering topics such as supervised learning, unsupervised learning, and deep learning. It also includes discussions of NLP applications such as text classification, question answering, and machine translation.
    Provides a comprehensive overview of NLP with Python and the NLTK library, covering a wide range of NLP tasks such as text preprocessing, feature engineering, and model evaluation. It also includes hands-on exercises and projects to help readers apply their knowledge.
    Provides a comprehensive overview of text mining with R, covering a wide range of NLP tasks such as text preprocessing, feature engineering, and model evaluation. It also includes hands-on exercises and projects to help readers apply their knowledge.
    Provides a practical guide to NLP with Python, covering a wide range of NLP tasks such as text preprocessing, feature engineering, and model evaluation. It also includes hands-on exercises and projects to help readers apply their knowledge.

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