UDEMY.Complete.Generative.AI.Course.With.Langchain.and.Huggingface.2025.BOOKWARE-MiMiR

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2026-02-01

NFO

Mímir, Keeper of the Well of Wisdom
  
  Complete Generative AI Course With Langchain and Huggingface

  https://www.udemy.com/course/complete-generative-ai-course-with-
  langchain-and-huggingface/

  Year         : 2025
  Language     : English
  Level        : All Levels
  Category     : Development
  Subcategory  : Data Science
  Duration     : 57h 37m
  Lectures     : 224
  Rating       : 4.6/5 (16,188 reviews)
  Students     : 111,714

  INSTRUCTOR(S)


  HEADLINE
    Complete Guide to Building, Deploying, and Optimizing
    Generative AI with Langchain and Huggingface


  WHAT YOU'LL LEARN
  * 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.


  REQUIREMENTS
  * Familiarity with Python programming language, including
    basic
    syntax, data structures, and functions.
  * Basic understanding of machine learning concepts, including
    supervised and unsupervised learning, and fundamental AI
    principles.
  * Basic knowledge of deep learning concepts and frameworks
    (e.g., TensorFlow or PyTorch) is advantageous but not
      strictly
    required.
  * Understanding of APIs and how to interact with them, as the
    course will involve integrating various APIs for model
    deployment and usage.
  * Ability to navigate and execute commands in a command line
    interface (CLI) or terminal.


  WHO IS THIS COURSE FOR
  * 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.


  DESCRIPTION
  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 CONTENT

  Chapter 1: Introduction
    1. Introduction-What We will Learn In This Course
    2. Course Materials
    3. Getting Started With VS Code
    4. Different Ways Of creating Python Environment
    5. Solve-Conda Not Recognized Issue
    6. Python Basics-Syntax And Semantics
    7. Variables In Python
    8. Basics DataTypes In Python
    9. Operators In Python

  Chapter 2: Python Control Flow
    10. Conditional Statements (if, elif, else)
    11. Loops In Python

  Chapter 3: Data Structures Using Python
    12. Lists and List Comprehension In Python
    13. Tuples In Python
    14. Dictionaries In Python
    15. Real World Use cases Of List

  Chapter 4: Functions In Python
    16. Getting Started With Functions
    17. More Coding Examples With Functions
    18. Lambda Function In Python
    19. Map Function In Python
    20. Filter Functions In Python

  Chapter 5: Importing, Creating Modules And Packages
    21. Import Modules And Packages In Python
    22. Standard Libraries Overview In Python

  Chapter 6: File Handling In Python
    23. File Operations With Python
    24. Working with File Paths

  Chapter 7: Exception Handling
    25. Exceptiion Handling With try except else and finally
        blocks

  Chapter 8: OOPS Classes And Objects
    26. Classes And Objects In Python
    27. Single And Multiple Inheritance
    28. Polymorphism In OOPS
    29. Encapulation In OOPS
    30. Abstraction In OOPS
    31. Magic Methods In Python
    32. Operator Overloading In Python

  Chapter 9: Streamlit With Python
    33. Getting Started With Streamlit
    34. Example Of ML APP With Streamlit

  Chapter 10: Machine Learning For NLP (Prerequisites)
    35. Roadmap To Learn NLP
    36. Practical Usecases Of NLP
    37. Tokenization and Basic Terminologies
    38. Tokenization Practicals
    39. Text Preprocessing Stemming Uing NLTK
    40. Text Preprocessing Lemmatization
    41. Text Preprocessing Stopwords
    42. Parts Of Speech Tagging Using NLTK
    43. Named Entity Recognition
    44. Whats Next
    45. One Hot Encoding
    46. Advantages and Disadvantages of OHE
    47. Bag Of Words Intuition
    48. Advantages and Disadvantages Of BOW
    49. BOW Implementation Using NLTK
    50. N Grams
    51. N gram Implementation USing NLTK
    52. TF-IDF Intuition
    53. Advantages and Disadvanatges OF TFidf
    54. TFIDF Practical Implementation
    55. Word Embeddings
    56. Word2vec Intuition
    57. Word2vec CBOW Detailed Explanation
    58. SkipGram Indepth Intuition
    59. Advantages OF Word2vec
    60. Average Word2Vec
    61. Word2vec Practical Implementation

  Chapter 11: Deep Learning For NLP(Prerequisites)
    62. Introduction To NLP In Deep Learning
    63. ANN VS RNN

  Chapter 12: Simple RNN Indepth Intuition
    64. RNN Forward Propogation With Time
    65. Simple RNN Backward Propogation
    66. Problems With RNN

  Chapter 13: ANN Project Implementation
    67. Discussing Classification Problem Statement And Setting
        Up Vs Code
    68. Feature Transformation Using Sklearn With ANN
    69. Step By Step Training With ANN With Optimizer and Loss
        Functions
    70. Prediction With Trained ANN Model
    71. Integrating ANN Model With Streamlit Web APP
    72. Deploying Streamlit web app with ANN Model
    73. ANN Regression Practical Implementation
    74. Finding Optimal Hidden Layers And Hidden Neurons In ANN

  Chapter 14: End To End Deep Learning Projects With Simple RNN
    75. Problem Statement
    76. Getting Started With Embedding Layers
    77. Implementing Word Embedding With Keras Tensorflow
    78. Loading And Understanding IMDB Datatset And Feature
        Engineering
    79. Training Simple RNN With Embedding Layers
    80. Prediction From Trained Simple RNN
    81. End To End Streamlit Web App Integrated With RNN And
        Deployment

  Chapter 15: LSTM RNN Indepth Intuition
    82. Why LSTM RNN
    83. LSTM RNN Architecture
    84. Forget Gate In LSTM RNN
    85. Input Gate And Candidate Memory In LSTM RNN
    86. Output Gate In LSTM RNN
    87. Training Process In LSTM RNN
    88. Variants Of LSTM RNN
    89. GRU RNN Indepth Intuition

  Chapter 16: LSTM And GRU End To End Deep Learning Project-
    Predicting Next Word
    90. Discussing Problem Statement
    91. Data Collection And Preprocessing
    92. LSTM Neural Network Model Training
    93. Prediction From LSTM Model
    94. Streamlit Webapp Integration With LSTM Trained Model
    95. GRU RNN Variant Impelmentation

  Chapter 17: Bidirerctional RNN Indepth Intuition
    96. Bidirectional RNN- Why To Use It?

  Chapter 18: Encoder And Decoder|Sequence To Sequence
    Architecture
    97. Indepth Intuition OF Encoder And Decoder-Sequence to
        Sequence Architecture
    98. Problems With Encoder and Decoder

  Chapter 19: Attention Mechanism-Seq2Seq Networks
    99. Attention Mechanism Indepth Architecture Explanation

  Chapter 20: Transformers
    100. Plan Of Action
    101. What and Why To Use Transformers
    102. Understanding The basic Architecture Of Encoder
    103. Self Attention Layer Working
    104. Multi Head Attention
    105. Feed Forward Neural Network With Multi Head Attention
    106. Positional Encoding Indepth Intuition
    107. Layer Normalization
    108. Layer Normalization Examples
    109. Complete Encoder Transformer Architecture
    110. Decoder-Plan Of Action
    111. Decoder-Masked Multi Head Attention
    112. Encoder and Decoder Multi Head Attention
    113. Decoder Final Linear And Softmax Layer

  Chapter 21: Introduction To Generative AI And LLM Models
    114. What is Generative AI- AI Vs ML Vs DL Vs Generative AI
    115. How Open AI ChatGPt or LLama3 LLM Models are Trained
    116. Evolution OF LLM Models
    117. All LLM Models Analysis

  Chapter 22: Introduction To Langchain For Generative AI
    118. Complete Langchain Ecosystem
    119. Creating Virtual Environment

  Chapter 23: Getting Started With Langchain And OpenAI
    120. Getting Started With Langchain And OpenAI

  Chapter 24: Important Components And Modules In Langchain
    121. Introduction To Basic Components And Modules In
         Langchain
    122. Data Ingestion With Documents Loaders
    123. Text Splitting Techniques-Recursive Character Text
         Splitter
    124. Text Splitting Technique-Character Text splitter
    125. Text Splitting Technique-HTML Header Text Splitter
    126. Text Splitting Technique-Recursive Json Splitter
    127. Introduction To OpenAI Embedding
    128. Ollama Embeddings
    129. Huggingface Embeddings
    130. VectorStores-FAISS
    131. VectorStore And Retriever- ChromaDB
    132. Going Forward

  Chapter 25: Getting Started With Open AI And Ollama
    133. Building Important Components Of Langchain
    134. Building GENAI Apps
    135. Understanding Retrievers and Chains
    136. Introduction To Ollama And Set Up
    137. Simple Gen AI App Using Ollama
    138. Tracking Gen AI App Using Langsmith

  Chapter 26: Building Basic LLM application Using
    LCEL(LangChain Expression Language)
    139. Getting Started With Open source Models Using Groq API
    140. Building LLM,Prompt And Stroutput Chains With LCEL
    141. Deploy Langserve Runnable And Chain As API

  Chapter 27: Building Chatbots With Conversation History Using
    Langchain
    142. Building Chatbot With Message History Using Langchain
    143. Working With Prompt Termplate And Message Chat History
         Using Langchain
    144. Managing the Chat Conversation History Using Langchain
    145. Working With VectorStore And Retriever

  Chapter 28: Conversational Q&A Chatbot With Message History
    146. Building Conversational Q&A Chatbot With Message
         History

  Chapter 29: Langchain Version V1 Updates
    147. Introduction To Langchain Updates Version V1
    148. Creating Virtual Environment
    149. Creating Agents With Langchain
    150. LLM Model Integration With Langchain
    151. Invoking And Batch Streaming With Langchain
    152. Implementing Tools in Langchain
    153. Messages Types In Langchain
    154. LLM Structured Output Using Pydantic
    155. LLM Structured Output Using TypeDict
    156. LLM structured Output Using Data Class
    157. Summarization Middleware Using Langchain
    158. Human In the loop Middleware Ussing Langchain

  Chapter 30: End To End Q&A Chatbot GEN AI App With
    159. Introduction To The Q&A Chatbot
    160. Creating Virtual Environment
    161. Creating Prompt Template And Integrating Open AI API
    162. Creating Streamlit Web App and Integrating Response
         With OpenAI API
    163. Q&A Chatbot With Ollama And Open Source Models

  Chapter 31: RAG Document Q&A With GROQ API And LLama3
    164. Introduction To Groq Cloud And LPU Inference Engine
    165. RAG Document Q&A With GROQ API And LLama3

  Chapter 32: Conversational Q&A Chatbot- Chat With Pdf Along
    With Chat History
    166. Demo of the Conversational Q&A Chatbot
    167. End To End Conversational Q&A Chatbot Implementation

  Chapter 33: Search Engine With Langchain Tools And Agents
    168. Introduction To Tools And Agents
    169. Creating Tools Using Langchain
    170. Executing Tools And LLM with Agent Executors
    171. End To End Search Engine GEN AI App using Tools And
         Agent With Open Source LLM

  Chapter 34: Gen AI Project-Chat With SQL DB With Langchain SQL
    Toolkit and Agentype
    172. Demo of the Project
    173. Preparing the Data For SQlite3 Database
    174. Preparing The Data For My SQL Database
    175. Creating the Streamlit Web app and Configuring the
         Databases
    176. Integrating Web App With Langchain SQL Toolkit And
         Agenttype

  Chapter 35: Text Summarization With Langchain
    177. Introduction To text summarization With Langchain
    178. Stuff Chain And Map Reduce Text Summarization Indepth
         Intuition
    179. Stuff And Map Reduce Summarization Impelmentation
    180. Refine Chain Summarization Intuition And Implementation

  Chapter 36: Gen AI Projects- Youtube Video And Website Url
    Content Summarization
    181. End To End Project Demo
    182. Implementing Youtube Video And Website Url Content
         Summarization GEN AI App

  Chapter 37: Text To Math Problem Solver Using Google Gemma 2
    183. Demo of the End to End Project
    184. End To End Text to Math Problem Solver Using Google
         Gemma2 Model Implementation

  Chapter 38: Huggingface And Langchain Integration
    185. Introduction To Huggingface And Langchain Integration
    186. Langchain And Huggingface Integration Practical
         Implementation
    187. End to End Gen AI Project With Langchain And
         Huggingface

  Chapter 39: Pdf Query RAG With Langchain And AstraDB
    188. End To End Project With PDf Query RAG With Langchain
         And AstraDB

  Chapter 40: MultiLanguage Code Assistant Using CodeLama
    189. End To End MultiLanguage Code Assistant Implementation

  Chapter 41: Deployment Of Gen AI Apps In Streamlit and
    Huggingspace
    190. Deployment OF Gen AI APP In Streamlit Cloud
    191. Deployment Of Gen AI App In Huggingface spaces

  Chapter 42: Generative AI With AWS(Bonus)
    192. Life Cycle Of Gen AI Project In AWS Cloud
    193. Introduction To AWS Bedrock With Implementation
    194. Document Q&A RAG With Langchain And Bedrock
    195. End To End Blog Generation Gen AI Using AWS Lambda And
         Bedrock
    196. Deployment Of Huggingface OpenSource LLM Models In AWS
         Sagemakers With Endpoints

  Chapter 43: Getting Started With Nvidia NIM and Langchain
    197. Building RAG Document Q&A With Nvidia NIM And Langchain

  Chapter 44: Creating Multi AI Agents Using CrewAI For Real
    World Usecases
    198. Youtube Videos To Blog Page Using CrewAI Agents

  Chapter 45: Hybrid Search RAG With Vector Database And
    Langchain
    199. Introduction To Hybrid Search
    200. Reciprocal Rank Fusion In Hybrid Search
    201. End To End Hybrid Search RAG With Pinecone db And
         Langchain

  Chapter 46: Introduction To Graph Databases And Cypher Query
    Language With Langchain
    202. Introduction to Graph DB Wwith Langchain
    203. What is Knowledge Graph
    204. Creating Your Neo4j AuraDB Database Instance
    205. RDBMS VS Graph Database
    206. Neo4j Property Graph Data Model
    207. Getting Started With Cypher Query Language
    208. Intermediate To Advance Cypher Query Language

  Chapter 47: Practical Implementation With Graphdb With
    Langchain
    209. Getting Started-Creating Environment
    210. Inserting Data In Graph DB With Python And Langchain
    211. Creating GraphQuery Chain With Langchain
    212. Prompting Statergies GraphDB With LLM

  Chapter 48: Detailed Intuition and Implementation Of
    Finetuning LLM Models
    213. What Is Quantization Indepth Intuition
    214. LORA And QLORA Indepth Mathematical Intuition
    215. Practrical Implementation fine Tuning Custom Data With
         Google Gemma Model

  Chapter 49: End To End finetuning LLM Models With Lamini
    Platform
    216. End To End Finetuning LLM Models With Lamini AI Cloud

  Chapter 50: Building Stateful, Multi-Actor Applications Using
    LangGraph
    217. Introduction To Langgraph
    218. Creating Chatbots Using Langgraph
    219. Creating Chatbots With External Tools Workflow With
         Langraph
    220. End to End Multi AI RAG Chatbots Using Langgraph And
         AstraDB

  Chapter 51: MCP
    221. Introduction To MCP
    222. Important Components Of MCP
    223. Communication Between Components
    224. Demo OF MCP With Claude Desktop

  Chapter 52: Role Play


  DATES
  Published    : 2024-07-09
  Last Updated : 2025-12-18

If you fear the truth, don?t come to my well.

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