Complete Generative AI Course With Langchain and Huggingface from Udemy

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.

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.

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

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

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

Show steps

Expand knowledge and skills in generative AI by exploring additional tutorials and resources beyond the course materials.

Browse courses on Generative AI

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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

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Engage with fellow students to discuss concepts, exchange ideas, and reinforce your understanding.

Browse courses on Generative AI

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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

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Solidify your understanding of generative AI techniques by completing hands-on practice exercises and projects.

Browse courses on Generative AI

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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

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Practice building and deploying generative AI models using Langchain and Huggingface

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Reinforce understanding of the concepts and techniques covered in the course by practicing building and deploying generative AI models.

Browse courses on Generative AI

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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

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Expand your knowledge and skills by following online tutorials and workshops that cover advanced topics related to generative AI.

Browse courses on Generative AI

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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

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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

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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

Show steps

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

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Showcase your proficiency by building a fully functional generative AI application that solves a real-world problem.

Browse courses on Generative AI

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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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Complete Generative AI Course With Langchain and Huggingface

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