Artificial Intelligence, Ml, Llms, Ai-Agents A-Z With Python

Learn AI, ML & Build Real-World AI Agent from ground up. Understand LLMs, Transformers, Embedding Models, Prompting.

What you'll learn

Understand AI, ML, and AI Agent development from scratch

Implement hands-on AI Agent solutions and master the ecosystem

Grasp core AI decision-making concepts: Policy, State, Value, Action

Understand AI's mathematical foundations, explained in simple English

Understand Markov Decision Processes for sequential AI decision-making

Implement Q-Learning and Reinforcement Learning for agent optimization

Grasp Transformer architecture and LLMs, which revolutionized the modern AI

Understand embedding models, which make data machine-understandable

Master various prompting techniques for generative AI solutions

Apply best practices for practical, efficient LLM usage

Implement AI Agents that autonomously perceive, reason, and act

Gain practical experience implementing working AI agents step-by-step

Requirements

Computer Access: You'll need a desktop or laptop running Windows, Linux, or Mac

Programming Basics: Familiarity with basic Python programming would be good

Math Foundation: A high school level understanding of mathematics is required

Interest to Learn: Be ready to learn and apply both theory and practical skills

Description

Interested in Machine Learning & AI Agents – understanding & Practice? Then this course is for you.This course has been designed by me having 18+ years of experience in the industry designing & developing software & firmware for companies like IBM & HPE. I am certified from IIT – Kanpur, India’s Premier Technological Institute, on Data science.In this course I have made sure that focus is on Understanding & Applying AI, ML, AI-Agent concepts & hands-on, which is the major Key in this astronomically growing field of AI & Machine Learning. There will be rarely any field that will be untouched by AI & ML very soon. Making sure we are on top of the latest with hands-on, is of utmost importance.I will be walking you through the fascinating world of AI, ML, LLMs & AI-Agents with, so that the understanding seeps deep in, which is what the industry looks for & rewards.I am a practitioner, very keen working on building a community which can drastically, transform existing tech-infra & build newer ones solving real-world problems, which matter. This course will enable you to think from creating newer systems or dramatically enhancing existing ones.We will be using Python during the course. I will also be covering essential Python programming along the way.What you’ll Learn:Learn AI, ML & Build Real-World AI Agents from the Ground UpWhether you're a beginner or an experienced developer stepping into the AI world, this course is designed to give you a strong foundation in AI, Machine Learning, and hands-on AI Agent development—taught from the perspective of a passionate corporate AI practitioner and educator.Getting Started: Understand the Ecosystem – AI, ML, Data Science & RoboticsWe’ll begin by demystifying the core concepts of Artificial Intelligence (AI), Machine Learning (ML), Data Science, and Robotics. Learn how these fields intersect, differ, and support each other.You’ll also explore key foundational ideas like Policy, State, Value, and Action, which are the basic building blocks of intelligent decision-making in AI systems—especially important when designing AI agents.Mathematics & Theories – Explained in Simple EnglishDon’t worry if you're not a math wizard! Complex theories are broken down in plain, beginner-friendly terms:· Markov Decision Processes (MDPs) – the framework behind decision-making models in AI.· Bellman Equation – how future rewards are estimated and optimized in RL systems.· Q-Learning & Reinforcement Learning – the logic behind how agents "learn" by trial, error, and rewards.Understanding these core ideas helps you to create intelligent systems that adapt, learn, and improve over time.LLMs & Transformers – The Brains Behind Modern AIDive into the technology behind large language models (LLMs) like ChatGPT and BERT. Learn:· What Transformers are, and how they revolutionized AI.· How Self-Attention Mechanisms enable LLMs to understand context and nuance.· A deep breakdown of key components:o Input Representationso Self-Attention & Multi-Head Attentiono Feedforward Neural Networks (FFN)o Layer Normalization & SoftmaxThis section will give you a strong grasp of the architecture that powers today’s most powerful AI systems.Embedding Models – Making Language & Data Machine-UnderstandableYou’ll learn why embedding models are critical for translating raw data (like text, images, and actions) into a form that AI models can understand. Will be covering:· The need for embeddings in modern AI· Different types of embedding models· How embeddings work and how they fuel understanding in LLMs and agentsUnderstanding embeddings is key for mastering AI applications like search, recommendation systems, and conversational agents.Prompting – The Art of Talking to Generative AIPrompting is a core skill in working with LLMs today. This module focuses on:Types of prompting (zero-shot, few-shot, chain-of-thought, etc.)Techniques to optimize responses, extract accurate information, and guide the model effectivelyBest practices for practical and efficient use of LLMs for automation, content generation, coding assistance, and moreAI Agents – Theory and Hands-On DevelopmentThen, bringing it all together to understand what AI Agents are - that can autonomously perceive, reason, and act. You’ll gain:· A strong theoretical understanding of how AI agents function· Practical experience in building working AI agents step-by-step· Insights into how agents use embeddings, transformers, and RL concepts to operate intelligently in real environmentsWhether you're aiming to automate tasks, build conversational bots, or create decision-making systems, this module will empower you to build agents that don’t just respond - they think & act.Course Updates & ExpansionI’m committed to keeping this course dynamic and up-to-date. In the coming weeks:· More hands-on AI agent projects will be added· Additional theoretical sessions on emerging topics in AI and ML will be included· I’ll also be incorporating student suggestions wherever relevant to make sure your learning needs are addressedWho this course is for:Anyone interested in AI, Machine Learning, & AI Agents.Aspiring AI & ML Beginners: Those new to AI who want a clear, jargon-free introduction to core concepts, including reinforcement learning and transformers.Software Developers & Engineers: Professionals looking to expand their skill set with practical AI/ML and real-world project experience.Business Executives and Managers: Professionals in leadership roles who are looking to deepen their understanding of embedding models, prompting strategies, and large language model functioning.Corporate & Enterprise Professionals: Team leads, managers, or consultants seeking insights from a seasoned AI practitioner on best practices, pitfalls, and scalable agent architectures.Career Changers & Students: Anyone aiming to change your career path and get into AI field with hands-on labs, portfolio-worthy projects, and a modern understanding of AI agent development.AI Enthusiasts & Innovators: Anyone excited by the “greatest revolution since the internet boom” and eager to build, deploy, and iterate on autonomous AI agents.Students who have at least high school knowledge in math and wanting to start a career in AI, Machine learning.Anyone who wants to add value to their career or business by using powerful Machine Learning tools.Why Take This Course?This course is not just about watching videos - it’s about building your AI intuition, mastering real-world tools, and preparing to contribute meaningfully to modern AI projects. If you're eager to understand and build the technology behind the AI revolution, this course is for you.Get started on the journey of learning, building, and innovating…Do you think you don’t have the math or coding background.This course breaks down even advanced topics like Markov Decision Processes, the Bellman equation, and Q-Learning in plain English, with step-by-step demos. You’ll build intuition before diving into formulas, so no prior AI experience is required.I won’t have time - online courses are a huge time-sink and many students drop out.Research shows that “too much time required” and “lectures that drag on” are top reasons for drop-outs. That’s why this course is organized into short, focused modules, plus bite-sized hands-on labs, so you can learn and apply concepts in under an hour even on a busy schedule.Most courses are all theory where’s the practical, real-world work?The course balances important theoretical lessons with coding exercises and career-ready projects. You’ll not only learn LLM internals and embedding math, but also be able to build functional AI agents.Thinking – If this will help with your career?Employers look for demonstrable skills. With this, you’ll have projects worth demonstrating - starting with simple AI agent-based ones to complex ones - that substantiates your learning and practice. Plus, as an industry-experienced practitioner, I share best practices and pitfalls to help you hit the ground running in enterprise environments.Enrol today and jump on to the fastest-growing fields of AI, ML, and AI-Agent development - your future self will thank you!Grab the greatest opportunity since the 1990s’ internet boom: opportunity like this takes decades to come by, jump in early to lead the way.Learn to build real-world AI projects with expert-led hands-on sessions, stand out in a competitive market, and stay ahead of the curve.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Starting up in AI

Lecture 2 Why To Learn AI now?

Lecture 3 Get the Big picture and overview of Core components

Section 3: Getting Into AI

Lecture 4 ML Basics - Policy and Action

Lecture 5 ML Basics - State Value Policy

Lecture 6 Bellman Equation

Lecture 7 ML Basics State-Value and Action-Value

Lecture 8 Reinforcement Learning Techniques and Classification

Section 4: Q - Learning

Lecture 9 What is Q-Learning?

Lecture 10 Q-Learning Practical Explainer

Lecture 11 Q-Learning Practical Session

Section 5: Understanding LLMs

Lecture 12 Understand the Architecture and Core components of LLMs

Lecture 13 How are LLMs built, training process, Attention mechanism

Lecture 14 How is LLM functionality built in, Optimization, Scaling & Evaluation

Section 6: Deepdive in Transformers

Lecture 15 What are the components of Transformers and how do they operate?

Lecture 16 What is input represtation, Tokenization, Embedding layer, Position Encoding

Lecture 17 Transformers - the Self-Attention Mechanism

Lecture 18 Transformers - Feedforward Networks, Multi-Head Attention

Lecture 19 Transformers - Residual Connections, Layer Normalization, Stacking Layers

Lecture 20 Transfomers - Summary, Advantages, Limitation and Going Forward

Section 7: Embedding Models

Lecture 21 Introduction to Embedding Models

Lecture 22 What are the different Types of Embedding Models

Lecture 23 How Do Embedding Models Work

Lecture 24 Applications of Embedding Models and Comparison to other ML models

Section 8: Mastering the Art and Science of Prompting

Lecture 25 What is prompting and Various Types of Prompting

Lecture 26 Prompting - One shot, Few shot, Chain-of-Thought

Lecture 27 Prefix-Prompting, JSON output prompting

Lecture 28 Role & Instruction-based prompting and Tips for Effective Prompting

Section 9: Practical Sessions - Building AI Agent

Lecture 29 AI Agent - Getting started & Setting up enviroment

Lecture 30 AI Agent - Building the first part of AI Agent

Lecture 31 AI Agent - Preparing data for our AI Agent

Lecture 32 AI Agent - Preditions using AI system for the Agent

Lecture 33 AI Agent - Fetching data from internet for our Agent

Lecture 34 AI Agent - Putting it all together to get AI Agent complete our intended task

Section 10: Conclusion and Next steps

Lecture 35 Completion and Next steps

Anyone interested in AI, Machine Learning, & AI Agents,Aspiring AI & ML Beginners: Those new to AI who want a clear, jargon-free introduction to core concepts, including reinforcement learning and transformers,Software Developers & Engineers: Professionals looking to expand their skill set with practical AI/ML and real-world project experience,Business Executives and Managers: Professionals in leadership roles who are looking to deepen their understanding of embedding models, prompting strategies, and large language model functioning,Corporate & Enterprise Professionals: Team leads, managers, or consultants seeking insights from a seasoned AI practitioner on best practices, pitfalls, and scalable agent architectures,Career Changers & Students: Anyone aiming to change the career path and get into AI field with hands-on labs, portfolio-worthy projects, and a modern understanding of AI agent development,AI Enthusiasts & Innovators: Anyone excited by the “greatest revolution since the internet boom” and eager to build, deploy, and iterate on autonomous AI agents,Students who have at least high school knowledge in math and wanting to start a career in AI, Machine learning,Anyone who wants to added value to their business by using powerful Machine Learning tools