Complete Generative AI Course With Langchain and Huggingface

Complete Generative AI Course With Langchain and Huggingface, available at $54.99, has an average rating of 4.69, with 203 lectures, based on 1547 reviews, and has 15541 subscribers.

You will learn about Learn to create advanced generative AI applications leveraging the Langchain framework and Huggingfaces 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 Huggingfaces 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. This course is ideal for individuals who are Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications. or Professionals looking to enhance their expertise in building and deploying generative AI models, particularly using Langchain and Huggingface. or Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models. It is particularly useful for Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications. or Professionals looking to enhance their expertise in building and deploying generative AI models, particularly using Langchain and Huggingface. or Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models.

Enroll now: Complete Generative AI Course With Langchain and Huggingface

Summary

Title: Complete Generative AI Course With Langchain and Huggingface

Price: $54.99

Average Rating: 4.69

Number of Lectures: 203

Number of Published Lectures: 203

Number of Curriculum Items: 203

Number of Published Curriculum Objects: 203

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to create advanced generative AI applications leveraging the Langchain framework and Huggingfaces 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 Huggingfaces 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.

Who Should Attend

Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications.

Professionals looking to enhance their expertise in building and deploying generative AI models, particularly using Langchain and Huggingface.

Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models.

Target Audiences

Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications.

Professionals looking to enhance their expertise in building and deploying generative AI models, particularly using Langchain and Huggingface.

Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models.

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction-What We will Learn In This Course

Lecture 2: Course Materials

Lecture 3: Getting Started With VS Code

Lecture 4: Different Ways Of creating Python Environment

Lecture 5: Solve-Conda Not Recognized Issue

Lecture 6: Python Basics-Syntax And Semantics

Lecture 7: Variables In Python

Lecture 8: Basics DataTypes In Python

Lecture 9: Operators In Python

Chapter 2: Python Control Flow

Lecture 1: Conditional Statements (if, elif, else)

Lecture 2: Loops In Python

Chapter 3: Data Structures Using Python

Lecture 1: Lists and List Comprehension In Python

Lecture 2: Tuples In Python

Lecture 3: Dictionaries In Python

Lecture 4: Real World Use cases Of List

Chapter 4: Functions In Python

Lecture 1: Getting Started With Functions

Lecture 2: More Coding Examples With Functions

Lecture 3: Lambda Function In Python

Lecture 4: Map Function In Python

Lecture 5: Filter Functions In Python

Chapter 5: Importing, Creating Modules And Packages

Lecture 1: Import Modules And Packages In Python

Lecture 2: Standard Libraries Overview In Python

Chapter 6: File Handling In Python

Lecture 1: File Operations With Python

Lecture 2: Working with File Paths

Chapter 7: Exception Handling

Lecture 1: Exceptiion Handling With try except else and finally blocks

Chapter 8: OOPS Classes And Objects

Lecture 1: Classes And Objects In Python

Lecture 2: Single And Multiple Inheritance

Lecture 3: Polymorphism In OOPS

Lecture 4: Encapulation In OOPS

Lecture 5: Abstraction In OOPS

Lecture 6: Magic Methods In Python

Lecture 7: Operator Overloading In Python

Chapter 9: Streamlit With Python

Lecture 1: Getting Started With Streamlit

Lecture 2: Example Of ML APP With Streamlit

Chapter 10: Machine Learning For NLP (Prerequisites)

Lecture 1: Roadmap To Learn NLP

Lecture 2: Practical Usecases Of NLP

Lecture 3: Tokenization and Basic Terminologies

Lecture 4: Tokenization Practicals

Lecture 5: Text Preprocessing Stemming Uing NLTK

Lecture 6: Text Preprocessing Lemmatization

Lecture 7: Text Preprocessing Stopwords

Lecture 8: Parts Of Speech Tagging Using NLTK

Lecture 9: Named Entity Recognition

Lecture 10: Whats Next

Lecture 11: One Hot Encoding

Lecture 12: Advantages and Disadvantages of OHE

Lecture 13: Bag Of Words Intuition

Lecture 14: Advantages and Disadvantages Of BOW

Lecture 15: BOW Implementation Using NLTK

Lecture 16: N Grams

Lecture 17: N gram Implementation USing NLTK

Lecture 18: TF-IDF Intuition

Lecture 19: Advantages and Disadvanatges OF TFidf

Lecture 20: TFIDF Practical Implementation

Lecture 21: Word Embeddings

Lecture 22: Word2vec Intuition

Lecture 23: Word2vec CBOW Detailed Explanation

Lecture 24: SkipGram Indepth Intuition

Lecture 25: Advantages OF Word2vec

Lecture 26: Word2vec Practical Implementation

Chapter 11: Deep Learning For NLP(Prerequisites)

Lecture 1: Introduction To NLP In Deep Learning

Lecture 2: ANN VS RNN

Chapter 12: Simple RNN Indepth Intuition

Lecture 1: RNN Forward Propogation With Time

Lecture 2: Simple RNN Backward Propogation

Lecture 3: Problems With RNN

Chapter 13: ANN Project Implementation

Lecture 1: Discussing Classification Problem Statement And Setting Up Vs Code

Lecture 2: Feature Transformation Using Sklearn With ANN

Lecture 3: Step By Step Training With ANN With Optimizer and Loss Functions

Lecture 4: Prediction With Trained ANN Model

Lecture 5: Integrating ANN Model With Streamlit Web APP

Lecture 6: Deploying Streamlit web app with ANN Model

Lecture 7: ANN Regression Practical Implementation

Lecture 8: Finding Optimal Hidden Layers And Hidden Neurons In ANN

Chapter 14: End To End Deep Learning Projects With Simple RNN

Lecture 1: Problem Statement

Lecture 2: Getting Started With Embedding Layers

Lecture 3: Implementing Word Embedding With Keras Tensorflow

Lecture 4: Loading And Understanding IMDB Datatset And Feature Engineering

Lecture 5: Training Simple RNN With Embedding Layers

Lecture 6: Prediction From Trained Simple RNN

Lecture 7: End To End Streamlit Web App Integrated With RNN And Deployment

Chapter 15: LSTM RNN Indepth Intuition

Lecture 1: Why LSTM RNN

Lecture 2: LSTM RNN Architecture

Lecture 3: Forget Gate In LSTM RNN

Lecture 4: Input Gate And Candidate Memory In LSTM RNN

Lecture 5: Output Gate In LSTM RNN

Instructors

Krish Naik
Chief AI Engineer

KRISHAI Technologies Private Limited
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 8 votes

2 stars: 14 votes

3 stars: 62 votes

4 stars: 470 votes

5 stars: 993 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!