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Krish Naik and KRISHAI Technologies Private Limited

Unlock the full potential of Generative AI with our comprehensive course, "Complete Generative AI Course with Langchain and Huggingface." This course is designed to take you from the basics to advanced concepts, providing hands-on experience in building, deploying, and optimizing AI models using Langchain and Huggingface. Perfect for AI enthusiasts, developers, and professionals, this course offers a practical approach to mastering Generative AI.

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Unlock the full potential of Generative AI with our comprehensive course, "Complete Generative AI Course with Langchain and Huggingface." This course is designed to take you from the basics to advanced concepts, providing hands-on experience in building, deploying, and optimizing AI models using Langchain and Huggingface. Perfect for AI enthusiasts, developers, and professionals, this course offers a practical approach to mastering Generative AI.

What You Will Learn:

  • Introduction to Generative AI:

    • Understand the fundamentals of Generative AI and its applications.

    • Explore the differences between traditional AI models and generative models.

  • Getting Started with Langchain:

    • Learn the basics of Langchain and its role in AI development.

    • Set up your development environment and tools.

  • Huggingface Integration:

    • Integrate Huggingface's state-of-the-art models into your Langchain projects.

    • Customize and fine-tune Huggingface models for specific applications.

  • Building Generative AI Applications:

    • Step-by-step tutorials on creating advanced generative AI applications.

    • Real-world projects such as chatbots, content generators, and data augmentation tools.

  • Deployment Strategies:

    • Learn various deployment strategies for AI models.

    • Deploy your models to cloud platforms and on-premise servers for scalability and reliability.

  • RAG Pipelines:

    • Develop Retrieval-Augmented Generation (RAG) pipelines to enhance AI performance.

    • Combine generative models with retrieval systems for improved information access.

  • Optimizing AI Models:

    • Techniques for monitoring and optimizing deployed AI models.

    • Best practices for maintaining and updating AI systems.

  • End-to-End Projects:

    • Hands-on projects that provide real-world experience.

    • Build, deploy, and optimize AI applications from scratch.

Who Should Take This Course:

  • AI and Machine Learning Enthusiasts

  • Data Scientists and Machine Learning Engineers

  • Software Developers and Engineers

  • NLP Practitioners

  • Students and Academics

  • Technical Entrepreneurs and Innovators

  • AI Hobbyists

By the end of this course, you will have the knowledge and skills to build, deploy, and optimize generative AI applications, leveraging the power of Langchain and Huggingface. Join us on this exciting journey and become a master in Generative AI.

Enroll now

What's inside

Learning objectives

  • Learn to create advanced generative ai applications leveraging the langchain framework and huggingface's state-of-the-art models.
  • Understand the architecture and design patterns for building robust generative ai systems.
  • Gain hands-on experience in deploying generative ai models to various environments, including cloud platforms and on-premise servers.
  • Explore different deployment strategies, ensuring scalability and reliability of ai applications.
  • Develop retrieval-augmented generation (rag) pipelines to enhance the performance and accuracy of generative models by integrating retrieval mechanisms.
  • Learn to seamlessly incorporate huggingface's pre-trained models into langchain applications, leveraging their powerful nlp capabilities.
  • Customize and fine-tune huggingface models to fit specific application requirements and use cases.
  • Work on real-world projects that illustrate the application of generative ai in various domains, such as chatbots, content generation, and data augmentation.

Syllabus

Introduction
Introduction-What We will Learn In This Course
Course Materials
Getting Started With VS Code
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Different Ways Of creating Python Environment
Solve-Conda Not Recognized Issue
Python Basics-Syntax And Semantics
Variables In Python
Basics DataTypes In Python
Operators In Python
Python Control Flow
Conditional Statements (if, elif, else)
Loops In Python
Data Structures Using Python
Lists and List Comprehension In Python
Tuples In Python
Dictionaries In Python
Real World Use cases Of List
Functions In Python
Getting Started With Functions
More Coding Examples With Functions
Lambda Function In Python
Map Function In Python
Filter Functions In Python
Importing, Creating Modules And Packages
Import Modules And Packages In Python
Standard Libraries Overview In Python
File Handling In Python
File Operations With Python
Working with File Paths
Exception Handling
Exceptiion Handling With try except else and finally blocks
OOPS Classes And Objects
Classes And Objects In Python
Single And Multiple Inheritance
Polymorphism In OOPS
Encapulation In OOPS
Abstraction In OOPS
Magic Methods In Python
Operator Overloading In Python
Streamlit With Python
Getting Started With Streamlit
Example Of ML APP With Streamlit
Machine Learning For NLP (Prerequisites)
Roadmap To Learn NLP
Practical Usecases Of NLP
Tokenization and Basic Terminologies
Tokenization Practicals
Text Preprocessing Stemming Uing NLTK
Text Preprocessing Lemmatization
Text Preprocessing Stopwords
Parts Of Speech Tagging Using NLTK
Named Entity Recognition
Whats Next
One Hot Encoding
Advantages and Disadvantages of OHE
Bag Of Words Intuition
Advantages and Disadvantages Of BOW
BOW Implementation Using NLTK
TF-IDF Intuition
Advantages and Disadvanatges OF TFidf
TFIDF Practical Implementation
Word Embeddings
Word2vec Intuition
Word2vec CBOW Detailed Explanation
SkipGram Indepth Intuition
Advantages OF Word2vec
Word2vec Practical Implementation
Deep Learning For NLP(Prerequisites)
Introduction To NLP In Deep Learning
ANN VS RNN
Simple RNN Indepth Intuition
RNN Forward Propogation With Time
Simple RNN Backward Propogation
Problems With RNN
ANN Project Implementation
Discussing Classification Problem Statement And Setting Up Vs Code
Feature Transformation Using Sklearn With ANN
Step By Step Training With ANN With Optimizer and Loss Functions
Prediction With Trained ANN Model
Integrating ANN Model With Streamlit Web APP
Deploying Streamlit web app with ANN Model
ANN Regression Practical Implementation
Finding Optimal Hidden Layers And Hidden Neurons In ANN
End To End Deep Learning Projects With Simple RNN
Problem Statement
Getting Started With Embedding Layers
Implementing Word Embedding With Keras Tensorflow
Loading And Understanding IMDB Datatset And Feature Engineering
Training Simple RNN With Embedding Layers
Prediction From Trained Simple RNN
End To End Streamlit Web App Integrated With RNN And Deployment
LSTM RNN Indepth Intuition
Why LSTM RNN
LSTM RNN Architecture
Forget Gate In LSTM RNN
Input Gate And Candidate Memory In LSTM RNN
Output Gate In LSTM RNN
Training Process In LSTM RNN
Variants Of LSTM RNN

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches generative AI methods and practices using Langchain and Huggingface, frameworks leading in industry
Develops specialized skills on building, deploying, and optimizing generative AI applications
Covers advanced concepts and hands-on experience in integrating Langchain and Huggingface into generative AI systems
Offers real-world projects such as chatbots, content generators, and data augmentation tools to provide practical knowledge
Includes Retrieval-Augmented Generation (RAG) methodologies to enhance the performance of generative AI systems
Emphasizes deployment strategies to ensure scalability and reliability of deployed generative AI models

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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 Complete Generative AI Course With Langchain and Huggingface with these activities:
Explore additional tutorials and resources on Generative AI using Langchain and Huggingface
Expand knowledge and skills in generative AI by exploring additional tutorials and resources beyond the course materials.
Browse courses on Generative AI
Show steps
  • Search for tutorials and documentation on Langchain and Huggingface websites.
  • Follow step-by-step tutorials to build and deploy generative AI models.
  • Explore community forums and discussion groups related to Langchain and Huggingface.
  • Experiment with different model architectures and hyperparameters to improve model performance.
Participate in study groups or discussion forums
Engage with fellow students to discuss concepts, exchange ideas, and reinforce your understanding.
Browse courses on Generative AI
Show steps
  • Identify or create study groups or discussion forums related to generative AI.
  • Actively participate in discussions.
  • Ask questions, share insights, and provide constructive feedback.
  • Collaborate on projects or assignments.
Complete hands-on practice exercises
Solidify your understanding of generative AI techniques by completing hands-on practice exercises and projects.
Browse courses on Generative AI
Show steps
  • Identify specific exercises or projects within the course.
  • Gather necessary resources and set up your development environment.
  • Implement the exercises or projects using Langchain and Huggingface.
  • Test and debug your code.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice building and deploying generative AI models using Langchain and Huggingface
Reinforce understanding of the concepts and techniques covered in the course by practicing building and deploying generative AI models.
Browse courses on Generative AI
Show steps
  • Create a new project in Langchain and set up your development environment.
  • Integrate Huggingface's pre-trained models into your Langchain project.
  • Train a generative AI model using Langchain and Huggingface.
  • Deploy your trained model to the cloud or on-premise servers.
Follow online tutorials and workshops
Expand your knowledge and skills by following online tutorials and workshops that cover advanced topics related to generative AI.
Browse courses on Generative AI
Show steps
  • Identify reputable sources for tutorials and workshops.
  • Select tutorials or workshops that align with your learning objectives.
  • Follow the instructions and complete the exercises provided.
  • Apply what you've learned to your own projects.
Create a blog post or article summarizing the key takeaways from the course
Solidify understanding and reinforce knowledge by creating a blog post or article that summarizes the key takeaways from the course.
Browse courses on Generative AI
Show steps
  • Identify the main concepts and techniques covered in the course.
  • Organize your thoughts and structure your article.
  • Write a clear and concise summary of each key concept.
  • Provide examples and illustrations to support your explanations.
  • Publish your article on a relevant platform.
Create a blog post or article
Demonstrate your understanding of generative AI concepts by creating a blog post or article that explains key terms, applications, and potential benefits.
Browse courses on Generative AI
Show steps
  • Choose a topic related to generative AI that you're interested in.
  • Research and gather information from credible sources.
  • Outline the structure of your blog post or article.
  • Write the content, ensuring clarity and thoroughness.
  • Proofread and edit your work for grammar, spelling, and flow.
Build a generative AI application
Showcase your proficiency by building a fully functional generative AI application that solves a real-world problem.
Browse courses on Generative AI
Show steps
  • Define the problem or need that your application will address.
  • Design the architecture and functionality of your application.
  • Develop the application using Langchain and Huggingface.
  • Test and deploy your application.

Career center

Learners who complete Complete Generative AI Course With Langchain and Huggingface will develop knowledge and skills that may be useful to these careers:

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