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Generative AI Masters 2026 - From Python to Gen AI (Udemy.com)

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

Created by: Dr. Satyajit Pattnaik

Last updated August 2026

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What you will 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.

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.4 out of 5higher than 28% of them
  • Reviews1,694#103 of 462 by number of reviews
  • Length50 hourslonger than 95% of them
  • Last updatedAugust 2026more recently than 85% of them

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Course 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!

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Instructor Details

Dr. Satyajit Pattnaik

Dr. Satyajit Pattnaik is a seasoned Data/AI professional based in Hong Kong, renowned for transforming complex data into actionable insights. With over 40,000 followers on LinkedIn and 115,000+ subscribers on YouTube, he combines his expertise in generative ai, machine learning, deep learning, and analytics to mentor aspiring data analysts, scientists & AI engineers.

A graduate of the Swiss School of Business and Management, Satyajit has spearheaded innovative research in voice-based patient classification, achieving remarkable accuracy rates.

He has trained over 200,000 professionals, including his YouTube channel, where he shares valuable insights and tutorials. Satyajit is also an advocate for generative AI, having conducted workshops that empower learners to harness cutting-edge technologies. His passion for education and commitment to the data community make him a valuable resource for anyone looking to advance their skills in data analytics.

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Reviews

4.4

1,694 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Kate Gribuste on 9/26/2026

The transition from fundamental sequence-to-sequence models to transformer pipelines was brilliantly structured. Every module felt practical, clear, and packed with relevant real-world software engineering techniques.

By Peddero Branco on 9/25/2026

This course is an absolute masterpiece for anyone wanting to learn artificial intelligence from scratch. Starting with basic Python and moving all the way up to building advanced Large Language Models is seamless. The hands-on lessons on ChatGPT and custom tools made complex coding concepts feel very easy to understand and apply.

By Rodrigo SanGue on 9/24/2026

An uncompromising deep-dive built explicitly for hardware-constrained AI system engineers. It trades shallow prompt engineering for parameter-efficient adaptation using 4-bit/8-bit quantization (QLoRA), custom task-oriented adapter merges, and automated evaluation harnesses. Equips technical leads to run localized, hyper-specialized open-weight LLMs with deterministic guardrails and strict GPU memory efficiency.

By Maria Luísa Francco on 9/21/2026

The lessons guide you step-by-step through setting up retrieval systems, optimizing prompts, and managing model context windows effortlessly. It delivers immense practical value for developers and data professionals aiming to upgrade their skills for 2026.

By Fernando Penaa on 9/20/2026

A great masterclass that guides you from basic programming to advanced AI tools step-by-step. It clearly shows you how to fine-tune AI models and link them to real-world tasks. The practical coding practice helped me build an impressive project portfolio for my resume.

By Iker Caballero on 9/19/2026

Essential, high-density training for AI engineers architecting enterprise-grade generative pipelines. Delivers surgical instruction on parameter-efficient fine-tuning (PEFT/LoRA), Retrieval-Augmented Generation (RAG) vector stores, and autonomous agent workflows to maximize throughput while suppressing model hallucinations.

By Andrea Vargas on 9/18/2026

An elite breakdown of foundational language processing and deep learning architectures. It seamlessly bridges classical NLP tokenization with modern self-attention mechanisms, offering an uncompromising blueprint for building, fine-tuning, and deploying transformer-based LLMs from scratch.

By Edith Henriksson on 9/8/2026

A flawless deep dive into modern NLP! Mastering self-attention mechanisms, transformer architectures, and pre-training techniques with PyTorch makes building production-ready language models remarkably clear and intuitive.

By Dhamodaran V T on 7/20/2026

need better clarity and coding resource document i don't find any which explained not shared as resource document

By Bernardo Carvalho on 1/4/2026

The course could improve in terms of both the materials and the presentation. There is a lot of handwritten text on the screen, often hard to read, which makes it difficult to follow. It would be better to use more slides/typed text and increase interactivity.

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Overall Score : 88 / 100