Generative AI Made Simple: Build Intelligent, Innovative Applications—No AI/ML Experience Needed
What you'll learn
Learners will master the core concepts and principles behind Generative AI, how LLMs work, and how they can be applied in real-world use cases.
Develop practical skills to design, architect, and implement smart applications leveraging a wide range of LLMs from both open and closed sources.
Learn how to effectively use popular tools including LangChain, HuggingFace, StreamLit, Ollama, and more
Understand and implement best practices when it comes to efficiency, scalability, and responsible use of generative AI in production environments.
Identify and navigate challenges specific to generative AI applications.
Prepare for intermediate-level technical interviews for roles in Generative AI.

Requirements
Basic knowledge of Python programming
Familiarity with Jupyter Notebooks is a plus
A personal machine (Windows, Linux, or Mac)
A stable internet connection

Description
Are you interested in learning generative AI, but feel intimidated by the complexities of AI and ML? If your answer is YES, then this course is for you! I structured this course based on my own experience learning Generative AI technology. Having faced the challenges firsthand, I designed this course to simplify the learning process. It's tailored specifically for those without an AI/ML background, helping you quickly get up to speed with generative AI.Designed specifically for IT professionals, developers, and architects with no prior AI/ML background, this course will empower you to build intelligent, innovative applications using Large Language Models (LLM). You'll gain practical, hands-on experience in applying cutting-edge generative AI technologies without the steep learning curve of mastering complex algorithms or mathematical theories.Here is an overview of course structure & coverage:Generative AI Foundations: Dive into the core concepts of Large Language Models (LLM), and learn how to work with powerful models like Google Gemini, Anthropic Claude, OpenAI GPT, and multiple open-source/Hugging Face LLMs.Building Generative AI Applications: Discover practical techniques for creating generative AI applications, including prompting techniques, inference control, in-context learning, RAG patterns (naive and advanced), agentic RAG, vector databases & much more.Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone and ChromaDB.Try out multiple LLM: Course doesn't depend on a single LLM for hands-on exercises, rather learners are encouraged to use multiple models for exercises so that they learn the nuances of their behavior.Learning Reinforcement: After each set of conceptual lessons, students are given exercises, projects, and quizzes to solidify their understanding and reinforce the material covered in previous lessons.Harnessing the Power of Hugging Face: Master the Hugging Face platform, including its tools, libraries, and community resources, to effectively utilize pre-trained models and build custom applications.Advanced Techniques: Delve into advanced topics like embeddings, search algorithms, model architecture, and fine-tunings to enhance your AI capabilities.Real-World Projects: Apply your knowledge through hands-on projects, such as building a movie recommendation engine and a creative writing workbench.Course Features18+ Hours of Video ContentHands-On Projects and Coding ExercisesReal-World ExamplesQuizzes for Learning ReinforcementGitHub Repository with SolutionsWeb-Based Course GuideBy the end of this course, you'll be well-equipped to leverage Generative AI for a wide range of applications, from natural language processing to content generation and beyond.Who Is This Course For?This course is perfect for:IT professionals, application developers, and architects looking to integrate generative AI into their applications.Students or professional preparing for interviews for the roles related to generative AIThose with no prior experience in AI/ML who want to stay competitive in today's rapidly evolving tech landscape.Anyone interested in learning how to build intelligent systems that solve real-world business problems using AI.Why Choose This Course?Raj structured this course based on his own experience in learning Generative AI technology. He applied his first hand knowledge of challenges faced in learning generative to create a structured course aimed at making it simple for anyone without AI/ML background to be able to get up to speed with generative ai fast.No AI/ML Background Needed: This course is designed for non-experts and beginners in AI/ML.Hands-On Learning: Engage in practical, real-world projects and coding exercises that bring AI concepts to life.Expert Guidance: Learn from Rajeev Sakhuja, a seasoned IT consultant with over 20 years of industry experience.Comprehensive Curriculum: Over 18 hours of video lessons, quizzes, and exercises, plus a web-based course guide to support you throughout your learning journey.Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone , FAISS, and ChromaDBWho should NOT take this course?Folks looking for deep dive into the internals of generative AI modelsLooking to gain understanding of mathematics behind the modelsIT professionals interested in DataSciences role

Overview
Section 1: Introduction
Lecture 1 Introductions
Lecture 2 Get the most from this course
Section 2: Setup development environment
Lecture 3 Section overview
Lecture 4 Setup development environment
Lecture 5 Quizzes, Exercises, and Projects
Lecture 6 Accessing the Large Language Models
Section 3: Generative AI : Fundamentals
Lecture 7 Section overview
Lecture 8 Intro to AI, ML, Neural Networks, and Gen AI
Lecture 9 Neurons, Neural & Deep Learning Networks
Lecture 10 Exercise: Try out a Neural Network for Solving Math Equations
Lecture 11 A Look at Generative AI Model as a Black Box
Lecture 12 Quiz: Fundamentals of Generative AI Models
Lecture 13 An Overview of Generative AI Applications
Lecture 14 Exercise: Setup Access to Google Gemini Models
Lecture 15 Introduction to Hugging Face
Lecture 16 Exercise: Checkout the Hugging Face Portal
Lecture 17 Exercise: Join the Community and Explore Hugging Face
Lecture 18 Quiz: Generative AI and Hugging Face
Lecture 19 Intro to Natural Language Processing (NLP, NLU, NLG)
Lecture 20 NLP with LLMs
Lecture 21 Exercise: Try Out NLP Tasks with Hugging Face Models
Lecture 22 Quiz: NLP with LLMs
Section 4: Generative AI applications
Lecture 23 Section overview
Lecture 24 Introduction to OLlama
Lecture 25 OLlama model hosting
Lecture 26 Model Naming Scheme
Lecture 27 Instruct, Embedding, and Chat Models
Lecture 28 Quiz: Instruct, Embedding, and Chat Models
Lecture 29 Next Word Prediction by LLM and Fill Mask Task
Lecture 30 Model Inference Control Parameters
Lecture 31 Randomness Control Inference Parameters
Lecture 32 Exercise: Setup Cohere Key and Try Out Randomness Control Parameters
Lecture 33 Diversity Control Inference Parameters
Lecture 34 Output Length Control Parameters
Lecture 35 Exercise: Try Out Decoding or Inference Parameters
Lecture 36 Quiz: Decoding Hyper-parameters
Lecture 37 Introduction to In-Context Learning
Lecture 38 Quiz: In-Context Learning
Section 5: Hugging Face Models : Fundamentals
Lecture 39 Section overview
Lecture 40 Exercise: Install & Work with Hugging Face Transformers Library
Lecture 41 Transformers Library Pipeline Classes
Lecture 42 Quiz: Hugging Face Transformers Library
Lecture 43 Hugging Face Hub Library & Working with Endpoints
Lecture 44 Quiz: Hugging Face Hub Library
Lecture 45 Exercise: Proof of Concept (PoC) for Summarization Task
Lecture 46 Hugging Face CLI Tools and Model Caching
Section 6: (Optional) Hugging Face Models : Advanced
Lecture 47 Section overview
Lecture 48 Model Input/Output and Tensors
Lecture 49 Hugging Face Model Configuration Classes
Lecture 50 Model Tokenizers & Tokenization Classes
Lecture 51 Working with Logits
Lecture 52 Hugging Face Models Auto Classes
Lecture 53 Quiz: Hugging Face Classes
Lecture 54 Exercise: Build a Question Answering System
Section 7: LLM challenges & prompt engineering
Lecture 55 Section overview
Lecture 56 Challenges with Large Language Models
Lecture 57 Model Grounding and Conditioning
Lecture 58 Exercise: Explore the Domain Adapted Models
Lecture 59 Prompt Engineering and Practices (1 of 2)
Lecture 60 Prompt Engineering and Practices (2 of 2)
Lecture 61 Quiz & Exercise: Prompting Best Practices
Lecture 62 Few-Shot & Zero-Shot Prompts
Lecture 63 Quiz & Exercise: Few-Shot Prompts
Lecture 64 Chain of Thought Prompting Technique
Lecture 65 Quiz & Exercise: Chain of Thought
Lecture 66 Self-Consistency Prompting Technique
Lecture 67 Tree of Thoughts Prompting Technique
Lecture 68 Quiz & Exercise: Tree of Thought
Lecture 69 Exercise: Creative Writing Workbench (v1)
Section 8: Langchain : Prompts, Chains & LCEL
Lecture 70 Section overview
Lecture 71 Prompt Templates
Lecture 72 Few-Shot Prompt Template & Example Selectors
Lecture 73 Prompt Model Specificity
Lecture 74 LLM Invoke, Streams, Batches & Fake LLM
Lecture 75 Exercise: Interact with LLM Using LangChain
Lecture 76 Exercise: LLM Client Utility
Lecture 77 Quiz: Prompt Templates, LLM, and Fake LLM
Lecture 78 Introduction to LangChain Execution Language
Lecture 79 Exercise: Create Compound Sequential Chain
Lecture 80 LCEL: Runnable Classes (1 of 2)
Lecture 81 LCEL: Runnable Classes (2 of 2)
Lecture 82 Exercise: Try Out Common LCEL Patterns
Lecture 83 Exercise: Creative Writing Workbench v2
Lecture 84 Quiz: LCEL, Chains and Runnables
Section 9: Dealing with structured responses from LLM
Lecture 85 Section overview
Lecture 86 Challenges with Structured Responses
Lecture 87 LangChain Output Parsers
Lecture 88 Exercise: Use the EnumOutputParser
Lecture 89 Exercise: Use the PydanticOutputParser
Lecture 90 Project: Creative Writing Workbench
Lecture 91 Project: Solution Walkthrough (1 of 2)
Lecture 92 Project: Solution Walkthrough (2 of 2)
Lecture 93 Handling Parsing Errors
Lecture 94 Quiz and Exercise: Parsers, Error Handling
Section 10: Datasets for model training, and testing
Lecture 95 Section overview
Lecture 96 Datasets for LLM Pre-training
Lecture 97 HuggingFace Datasets and Datasets Library
Lecture 98 Exercise: Use Features of Datasets Library
Lecture 99 Exercise: Create and Publish a Dataset on Hugging Face
Section 11: Vectors, embeddings & semantic search
Lecture 100 What is the Meaning of Contextual Understanding?
Lecture 101 Building Blocks of Transformer Architecture
Lecture 102 Intro to Vectors, Vector Spaces, and Embeddings
Lecture 103 Measuring semantic similarity with distance
Lecture 104 Quiz: Vectors, Embeddings, Similarity
Lecture 105 Sentence transformer models (SBERT)
Lecture 106 Working with sentence transformers
Lecture 107 Exercise: Work with Classification and Mining Tasks
Lecture 108 Creating embeddings with LangChain
Lecture 109 Exercise: CacheBackedEmbeddings Classes
Lecture 110 Lexical, semantic, and kNN search
Lecture 111 Search Efficiency and Search Performance Metrics
Lecture 112 Search Algorithms, Indexing, ANN, FAISS
Lecture 113 Quiz & Exercise: Try Out FAISS for Similarity Search
Lecture 114 Search Algorithm: Local Sensitivity Hashing (LSH)
Lecture 115 Search Algorithm: Inverted File Index (IVF)
Lecture 116 Search Algorithm: Product Quantization (PQ)
Lecture 117 Search Algorithm: HNSW (1 of 2)
Lecture 118 Search Algorithm: HNSW (2 of 2)
Lecture 119 Quiz & Exercise: Search Algorithms & Metrics
Lecture 120 Project: Build a Movie Recommendation Engine
Lecture 121 Benchmarking ANN Algorithms
Lecture 122 Exercise: Benchmark the ANN Algorithms
Section 12: Vector databases
Lecture 123 Challenges with semantic search libraries
Lecture 124 Introduction to Vector Databases
Lecture 125 Exercise: Try out ChromaDB
Lecture 126 Exercise: Custom embeddings
Lecture 127 Chunking, Symmetric & Asymmetric Searches
Lecture 128 LangChain Document Loaders
Lecture 129 LangChain Text Splitters for Chunking
Lecture 130 LangChain Retrievers & Vector stores
Lecture 131 Search Scores and Maximal-Marginal-Relevancy (MMR)
Lecture 132 Project: Pinecone Adoption @ Company
Lecture 133 Quiz: Vector Databases, Chunking, Text Splitters
Section 13: Conversation User Interface
Lecture 134 Introduction to Streamlit Framework
Lecture 135 Exercise: Build a HuggingFace LLM Playground
Lecture 136 Building Conversational User Interfaces
Lecture 137 Exercise: Build a Chatbot with Streamlit
Lecture 138 LangChain Conversation Memory
Lecture 139 Quiz & Exercise: Building Chatbots with LangChain
Lecture 140 Project: PDF Document Summarizer Application
Section 14: Advanced Retrieval Augmented Generation
Lecture 141 Introduction to Retrieval Augmented Generation (RAG)
Lecture 142 LangChain RAG Pipelines
Lecture 143 Exercise: Build Smart Retriever with LangChain
Lecture 144 Quiz: RAG and Retrievers
Lecture 145 Pattern: Multi Query Retriever (MQR)
Lecture 146 Pattern: Parent Document Retriever (PDR)
Lecture 147 Pattern: Multi Vector Retriever (MVR)
Lecture 148 Quiz: MQR, PDR and MVR
Lecture 149 Ranking, Sparse, Dense & Ensemble Retrievers
Lecture 150 Pattern: Long Context Reorder (LCR)
Lecture 151 Quiz: Ensemble & Long Context Retrievers
Lecture 152 Pattern: Contextual Compressor
Lecture 153 Pattern: Merger Retriever
Lecture 154 Quiz: Contextual Compressors and Merger Retriever Patterns
Section 15: Agentic RAG
Lecture 155 Introduction to Agents, Tools, and Agentic RAG
Lecture 156 Exercise: Build a Single-Step Agent without LangChain
Lecture 157 LangChain Tools and Toolkits
Lecture 158 Quiz: Agents, Tools & Toolkits
Lecture 159 Exercise: Try Out the FileManagement Toolkit
Lecture 160 How Do We Humans & LLMs Think?
Lecture 161 ReACT Framework & Multi-Step Agents
Lecture 162 Exercise: Build Question/Answering ReACT Agent
Lecture 163 Exercise: Build a Multi-Step ReACT Agent
Lecture 164 LangChain Utilities for Building Agentic-RAG Solutions
Lecture 165 Exercise: Build an Agentic-RAG Solution using LangChain
Lecture 166 Quiz: Agentic RAG and ReAct
Solution/Application Architects: Professionals looking to design cutting-edge applications that leverage LLMs to create more interactive, efficient, and intelligent solutions.,Application Developers: Interested in acquiring hands-on experience with tools, techniques, and frameworks to build AI-driven applications that can understand, generate, and respond intelligently to user inputs.,IT Professionals: Individuals aiming to transition into the growing field of Generative AI by gaining foundational knowledge and practical skills in AI-powered application development.,Data Scientists: Data professionals who want to expand their expertise by learning how to apply generative AI models to create sophisticated applications, beyond traditional data analysis and machine learning.

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