UDEMY.Generative.AI.Masters.From.Python.to.Gen.AI.2026.BOOKWARE-MiMiR

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2026-02-13

NFO

Mímir, Keeper of the Well of Wisdom
  
  Generative AI Masters 2026 - From Python to Gen AI

  https://www.udemy.com/course/mastering-generative-ai/

  Year         : 2026
  Language     : English
  Level        : All Levels
  Category     : Development
  Subcategory  : Data Science
  Duration     : 47h 56m
  Lectures     : 216
  Rating       : 4.6/5 (895 reviews)
  Students     : 22,439

  INSTRUCTOR(S)


  HEADLINE
    Master Generative AI with hands-on LLMs, ChatGPT, LangChain,
    RAG, Fine Tuning and real-world projects.


  WHAT YOU'LL LEARN
  * Build a solid foundation in Python programming to
    effectively
    implement AI concepts and applications.
  * Understand the complete pipeline of Natural Language
    Processing, from data preprocessing to model deployment.
  * Learn how transformer models revolutionize NLP tasks, and
    how
    to leverage them for various applications.
  * Explore the essentials of Large Language Models (LLMs) and
    their applications in generative tasks.
  * Gain hands-on experience with Retrieval-Augmented Generation
    (RAG) and Langchain for building advanced AI applications.
  * Develop skills in crafting effective prompts to optimize
    model
    performance and achieve desired outputs.
  * Learn how to utilize vector databases for efficient storage
    and retrieval of embeddings in AI projects.


  REQUIREMENTS
  * Basic knowledge on Machine Learning is beneficial but not
    mandatory


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


  DESCRIPTION
  Unlock the potential of Generative AI with our comprehensive
  course, "Gen AI Masters 2025 - From Python To LLMs and
  Deployment" This course is designed for both beginners and
  seasoned developers looking to deepen their understanding of
    the
  rapidly evolving field of artificial intelligence. Learn how
    to
  build Generative AI applications using Python and LLMs.
  Understand prompt engineering, explore vector databases like
  FAISS, and deploy real-world AI chatbots using RAG
    architecture.
  In this course, you will explore a wide range of essential
  topics, including: Python Programming: Learn the fundamentals
    of
  Python, the go-to language for AI development, and become
  proficient in data manipulation using libraries like Pandas
    and
  NumPy. Natural Language Processing (NLP): Dive into the world
    of
  NLP, mastering techniques for text processing, feature
  extraction, and leveraging powerful libraries like NLTK and
  SpaCy. Deep Learning and Transformers: Understand the
  architecture of Transformer models, which are at the heart of
  many state-of-the-art AI applications. Discover the principles
  of deep learning and how to implement neural networks using
  TensorFlow and PyTorch. Large Language Models (LLMs): Gain
  insights into LLMs, their training, fine-tuning processes
  (including PEFT, LoRA, and QLoRA), and learn how to
    effectively
  use these models in various applications, from chatbots to
  content generation. Retrieval-Augmented Generation (RAGs):
  Explore the innovative concept of RAG, which combines
    retrieval
  techniques with generative models to enhance AI performance.
  You'll also learn about RAG evaluation methods, including the
  RAGAS framework, BLEU, ROUGE, BARScore, and BERTScore. Prompt
  Engineering: Master the art of crafting effective prompts to
  improve interactions with LLMs and optimize outputs for
    specific
  tasks. Vector Databases: Discover how to implement and utilize
  vector databases for storing and retrieving high-dimensional
  data, a crucial skill in managing AI-generated content. The
  course culminates in a Capstone Project, where you will apply
  everything you've learned to solve a real-world problem using
  Generative AI techniques. Projects List: AI Career Coach: A
  personalized chatbot that guides users in career development
    and
  job search strategies using real-time data and insights.
  AI Powered Automated Claims Processing: An intelligent system
  that streamlines insurance claims by automating data
    extraction
  and decision-making processes. Chat Scholar Chatbot + Essay
  Grading System: An interactive chatbot that assists students
  with writing and provides AI-driven grading and feedback on
  essays. Research RAG Chatbot: A research assistant chatbot
    that
  retrieves relevant academic information and generates
    summaries
  based on user queries. Sustainability Chatbot (GROK AI): An
    eco-
  focused chatbot that educates users on sustainable practices
    and
  provides actionable tips for reducing their carbon footprint.
    If
  you have a specific project idea in mind, feel free to share
    it,
  and we will do our best to bring your vision to life. By the
    end
  of this course, you will have a solid foundation in Generative
  AI and the skills to implement complex AI solutions. Whether
  you're looking to enhance your career, transition into AI
  development, or simply explore this fascinating field, this
  course is your gateway to mastering Generative AI. Enroll now
  and take the first step toward becoming an expert in
    Generative
  AI!


  COURSE CONTENT

  Chapter 1: Introduction to the Course
    1. Introduction
    2. Course Resources

  Chapter 2: Python begins!!
    3. Welcome to the module on Python
    4. Introduction
    5. Datatypes & Operators
    6. Variables & Keywords

  Chapter 3: Python Data Structures
    7. Lists
    8. Tuples
    9. Sets
    10. Dictionary

  Chapter 4: Python File Handling, Loops & Functions
    11. Loops & Iterations
    12. Functions in Python
    13. Map, Reduce & Filter Functions
    14. File Handling

  Chapter 5: Control Structures & OOPs
    15. Control Structures
    16. Object Oriented Programming Concepts

  Chapter 6: Python for Data Science & Analysis
    17. Pandas
    18. Numpy

  Chapter 7: Python for Data Visualization
    19. Data Visualization
    20. Matplotlib
    21. Seaborn

  Chapter 8: Introduction to NLP (Pre-Requisite)
    22. Welcome to the module on NLP
    23. Introduction
    24. Introduction (Part II)
    25. NLP Key Challenges
    26. Linguistics

  Chapter 9: NLP Basics (Pre-Requisite)
    27. Case Folding
    28. Special Character Removal
    29. Handling Contractions
    30. Tokenization
    31. Stop Words Removal
    32. nGrams
    33. Vectorization
    34. Word Embeddings
    35. Bag of Words (Theory)
    36. Bag of Words (Practicals)
    37. TF-IDF (Theory)
    38. TF-IDF (Practicals)
    39. Part of Speech Tagging & NER
    40. Named Entity Recognition (Practicals)

  Chapter 10: Word Embeddings (Pre-Requisite)
    41. Word2Vec Intro
    42. Word2Vec Part 2
    43. Pretrained Word2Vec
    44. Word2Vec INTUITION
    45. Word2Vec 50Features
    46. Word2Vec CBOW
    47. Word2Vec SkipGrams
    48. GloVe
    49. fastText
    50. Cosine Similarity

  Chapter 11: NLP Neural Networks (Pre-Requisites)
    51. LSTM Part 1
    52. LSTM Part 2 (Architecture)
    53. LSTM Part 3 (Deep Dive Architecture)
    54. LSTM Part 4 (Pointwise Operations)
    55. LSTM Part 5 forgetGate
    56. LSTM Part 6 inputGate
    57. LSTM Part 7 outputGate
    58. LSTM Part 8 Practicals
    59. LSTM Part 9 Practicals
    60. LSTM Part 10 Practicals
    61. GRU Part 1
    62. GRU Part 2
    63. GRU Part 3 (resetGate)
    64. GRU Part 4 (updateGate)
    65. GRU Part 5 Practicals
    66. Bi Directional RNN

  Chapter 12: Deep Learning (Pre-Requisite)
    67. Welcome to the module on DL
    68. Introduction to DL
    69. Understanding Deep Learning
    70. What is a Neuron
    71. Activation Functions
    72. Step Function
    73. Linear Function
    74. Sigmoid Function
    75. TanH Function
    76. ReLU Function
    77. Backpropagation vs Forward Pass
    78. Gradient Descent
    79. ANN Intuition
    80. ANN (Practicals)
    81. ANN (Hyper Parameter Optimization)
    82. CNN - Steps in CNN
    83. CNN - What is CNN
    84. CNN - Architecture Explained
    85. CNN - Image Augmentation
    86. CNN - Batch size vs iterations vs epochs
    87. CNN - Code Implementation of CNN
    88. CNN - Model Summary & Parameters
    89. CNN - Hands on Project (XRAY Image Classification)
    90. RNN - Basics
    91. RNN - Types of RNN
    92. RNN - Vanishing Gradient vs Exploding Gradient Problem
    93. RNN - LSTMs
    94. RNN - LSTMs (Practicals)
    95. Pre Trained Models (Theory)
    96. Pre Trained Models (Practicals)
    97. VGG16
    98. MobileNet
    99. Transfer Learning
    100. Final Project + Streamlit

  Chapter 13: Transformers (Pre-Requisite)
    101. Welcome to the journey of Gen AI
    102. Transformers Types
    103. What are Transformers
    104. Self Attention is all you need!!
    105. Encoder Architecture
    106. Contextual Embeddings
    107. Decoder Architecture

  Chapter 14: Encoder Only Architecture (Pre-Requisite)
    108. Introduction to BERT
    109. BERT Configurations
    110. BERT Fine Tuning
    111. BERT Pre Training (Masked LM)
    112. Input Embeddings BERT
    113. ARLM vs AELM
    114. RoBERTa
    115. DistilBERT
    116. AlBERT

  Chapter 15: Decoder Only Architecture
    117. Introduction to GPT
    118. GPT Architecture
    119. GPT (Masked Multi Head Attention)
    120. GPT Blocks
    121. GPT Training

  Chapter 16: LLM Basics - Tokens, Context Window, Prompt,
    Prompt Tuning etc.
    122. What are Tokens
    123. Context Window
    124. What is a Prompt?
    125. Prompt Engineering
    126. Prompt Tuning
    127. Prompt Structures

  Chapter 17: RAGs (Retrieval Augmented Generation)
    128. Introduction to RAGs
    129. RAGs - What and Why
    130. RAGs - Use Cases
    131. RAGs - Research Paper
    132. RAGs Architectures + VectorDB Architecture
    133. RAGs - Detailed Architecture
    134. RAGs - Research Paper based Chatbot using Open AI
         (Practicals)

  Chapter 18: LangChain
    135. LangChain

  Chapter 19: Prompt Engineering
    136. Introduction to Prompt Engineering
    137. Types of Prompting
    138. Few Shot Limitations
    139. Chain of Thoughts Prompting

  Chapter 20: Vector Databases vs Vector Index
    140. Vector Databases
    141. Vector Index vs Vector Database
    142. How Vector db works
    143. Vector Database (Practicals)
    144. LSH

  Chapter 21: Model Overview: Ollama
    145. Model Overview
    146. Getting Started with Ollama
    147. Model Testing
    148. Python Implementation
    149. RAG Systems with Ollama
    150. RAG Systems with Ollama (Practicals)

  Chapter 22: Model Overview: OpenAI and xAI
    151. LLM APIs Overview
    152. RAG Systems with xAI: LLM APIs
    153. RAG Systems with xAI: LLM APIs (Practicals)

  Chapter 23: Fine Tuning
    154. Introduction
    155. RAGs vs Fine Tuning
    156. When to use RAG/Fine-Tuning?
    157. Fine Tuning
    158. PEFT and Quantization
    159. LoRA (Phase I - Low Rank)
    160. LoRA (Phase II - Adapters)
    161. LoRA (Phase III - Low Rank + Adapters)
    162. qLoRA (Quantized LoRA)

  Chapter 24: RAG Assessment and Evaluation Metrics
    163. Introduction
    164. Introduction to RAG Evaluation
    165. N-gram Metrics (BLEU)
    166. N-gram Metrics (ROUGE)
    167. N-gram Metrics (METEOR)
    168. Intrinsic Metrics (PPL Score)
    169. Intrinsic Metrics (PPL Score) Practicals
    170. Model-based Metrics (BARTScore vs BERTScore)
    171. RAGAS Framework (RAG Assessment)
    172. (PRACTICALS) Rag Evaluation by RAGAS
    173. (PRACTICALS) Rag Evaluation by BLEU, ROUGE
    174. (PRACTICALS) Rag Evaluation by BERTScore, BARTScore

  Chapter 25: Deployment of Gen AI Applications
    175. Introduction to Deployment (ML, DL, NLP etc)
    176. Introduction to Flask
    177. Building a basic Flask App
    178. Model Building (Breast Cancer)
    179. Flask App (Breast Cancer)
    180. Role of Cloud in Deployment (AWS)
    181. AWS Deployment of ML/DL Application
    182. Gen AI Model Deployment
    183. Architecture Diagram
    184. Model Serving
    185. Next Steps
    186. Air India Chatbot
    187. Code - PDF Based
    188. Code - Directory Based
    189. Streamlit App
    190. AWS EC2 Deployment

  Chapter 26: Gen AI Projects
    191. Project 1: Research RAG Chatbot
    192. Project 2: Chat Scholar Chatbot + Essay Grading System
    193. Project 3: AI Powered Insurance Claims Processing
    194. Project 4: AI Career Coach (Part 1)
    195. Project 4: AI Career Coach (Part 2)
    196. Project 4: AI Career Coach (Part 3)
    197. Project 5: Sustainability Chatbot (xAI's Grok-3)
    198. Project 6: Multi PDF RAG Chatbot built on Web Scraped
         Data
    199. Project 7: Text-to-SQL Chatbot: Part 1
    200. Project 7: Text-to-SQL Chatbot: Part 2
    201. Project 7: Text-to-SQL Chatbot: Part 3
    202. Project 7: Text-to-SQL Chatbot: Part 4
    203. Project 7: Text-to-SQL Chatbot: Part 5
    204. Project 7: Text-to-SQL Chatbot: Part 6
    205. Project 7: Text-to-SQL Chatbot: Part 7

  Chapter 27: Interview Prep (NEW SECTION)
    206. Gen AI Interview Guide
    207. Gen AI Interview #1
    208. Gen AI Interview #2
    209. Gen AI Interview #3
    210. Gen AI Interview #4
    211. Gen AI Interview #5
    212. Gen AI Interview #6
    213. Gen AI Interview #7
    214. Gen AI Interview #8
    215. Gen AI Interview #9
    216. Gen AI Interview #10


  DATES
  Published    : 2024-09-05
  Last Updated : 2026-02-03

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