Gen Ai: From Basics To Advanced Level

Master generative AI concepts, from basics to advanced, and gain practical experience with real-world projects

What you'll learn

Understand AI Basics

Explore Generative AI Models

Data Collection & Prep Skills

Create with GANs (Generative Adversarial Networks)

Text Generation Techniques

Train Models with Real Datasets

Fine-Tuning and Improving Models

Requirements

Some familiarity with Python programming is helpful, as Python will be used for creating AI models in the course.

Description

Generative AI: From Basics to Expert with Hands-On is a comprehensive course designed to take students through the exciting world of generative artificial intelligence. Starting with foundational concepts, students will learn the basics of Generative AI, including an introduction to Large Language Models (LLMs) and the importance of prompt engineering, a crucial skill for guiding AI responses. The course dives deeper into essential topics such as word embeddings and artificial neural networks, which form the backbone of generative models. Through hands-on practice, students will work with popular frameworks like Hugging Face and Lang Chain to apply LLMs in real scenarios, such as processing data from PDFs or Wikipedia and creating meaningful outputs. Students will also learn Retrieval-Augmented Generation, applying LLMs to private datasets to provide customized solutions. Beyond that, they’ll build intelligent agents capable of performing tasks independently, a skill that has real-world applications in automation and productivity.Additionally, the course covers advanced theories, such as deep learning algorithms and Transformers, offering insights into how these technologies reshape content creation, code generation, and translation. Generative AI skills are in high demand across industries, making this course a powerful step toward a career in this transformative field. Through this hands-on approach, students will be prepared to leverage generative AI’s potential and gain valuable expertise for future opportunities.

Overview

Section 1: Introduction to Gen AI

Lecture 1 Introduction

Lecture 2 Work with LLM and prompt engineering

Section 2: Machine Learning

Lecture 3 Linear Regression Analysis

Lecture 4 Logistic Regression

Lecture 5 Assignment-1

Lecture 6 Assignment-1 Solution

Section 3: Artificial Neural Networks (ANN)

Lecture 7 ANN

Lecture 8 ANN algorithm

Section 4: Word Embeddings

Lecture 9 Introduction to Word Embedding

Lecture 10 Word to Vector

Lecture 11 Assignment-2

Lecture 12 Assignment-2 Solution

Section 5: Hugging Face

Lecture 13 Introduction to Hugging Face

Lecture 14 Hugging Face Models

Lecture 15 Assignment-3

Lecture 16 Assignment-3 Solution

Section 6: LLM Life Hacking

Lecture 17 Open source LLM

Lecture 18 Basics of Lang Chain-1

Lecture 19 Basics of Lang Chain-2

Lecture 20 Assignment -4

Lecture 21 Assignment -4 solution

Lecture 22 Lang Chain Models IO-1

Lecture 23 Lang Chain Models IO-2

Lecture 24 Assignment -5

Lecture 25 Assignment-5 Solution

Lecture 26 Introduction to RAG (Retrieval Augmented Generation)

Lecture 27 RAG Validation

Lecture 28 Assignment -6

Lecture 29 Assignment -6 Solution

Lecture 30 Mastering Chatbots & Memory with Lang chain

those who want to expand their skills into the growing AI field and work with advanced models will find this course useful.