Full Stack Generative Ai: Deep Learning, Cnn, Llm Agentic Ai

Python, NumPy, Pandas, Matplotlib, Deep Learning, Generative AI : GAN, VAE, LLMs, RAG, MCP, ACP, A2A, Agentic AI & more

What you'll learn

Design and implement Generative AI models such as GANs, VAEs, Diffusion Models, and Large Language Models, including Retrieval-Augmented Generation (RAG).

Build autonomous AI agents using Agentic AI frameworks like LangChain and apply protocols such as MCP, ACP, and A2A

Understand and train deep learning models, building a strong foundation for advanced AI concepts.

Write Python programs and perform data manipulation and visualization using NumPy, Pandas, and Matplotlib.

Requirements

No prior programming or AI experience is required, this course starts from the basics. A basic understanding of high school mathematics (algebra, probability, and functions) will be helpful. Access to a computer with an internet connection. Curiosity, consistency, and a willingness to learn by building hands-on projects.

Description

This comprehensive course is your one-stop guide to learn Python Basics, Popular Data Manipulation Libraries, Deep Learning Fundamentals, Popular Generative AI Models, Large Language Models and Agentic AI frameworks, all in one place. Whether you're a beginner exploring the world of AI or a developer looking to level up, this course takes you from the ground up and beyond.We begin with Python fundamentals and dive into essential data libraries like NumPy, Pandas, and Matplotlib for effective data handling and visualization. Then, we advance into Deep Learning, building and training neural networksMode to understand the core mechanics behind AI.Generative AI is a subset of Deep Learning. Without a solid understanding of Deep Learning fundamentals, learning Generative AI becomes difficult and often confusing. That’s why I’ve combined the most essential parts from one of my previous Deep Learning courses into this course. This ensures that you build a strong foundation before diving into advanced Generative AI topics.Once the Deep Learning Fundamentals is complete, You’ll then explore the rapidly evolving field of Generative AI:From training your own GANs and VAEs, to working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Diffusion Models, this course offers hands-on projects and intuitive explanations.Finally, we introduce you to the next frontier: Agentic AI. Learn about intelligent agent architectures such as MCP, ACP, and A2A, and use cutting-edge frameworks like LangChain to build autonomous, goal-driven AI agents.What You’ll LearnPython programming basics and data manipulation using NumPy and PandasData visualization using MatplotlibFundamentals of Deep Learning and neural network trainingBuilding Generative AI models: GANs, VAEs, LLMs, and Diffusion ModelsImplementing Retrieval-Augmented Generation (RAG)Understanding and applying Agentic AI Protocols: MCP, ACP, A2AWorking with popular Agentic AI frameworks like LangChainRequirementsNo prior programming or AI experience is requiredA basic understanding of high-school math is helpfulAccess to a computer with internet connectionCuriosity and a willingness to learn by building real-world projectsThe code, and jupyter notebook files used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.So that's all for now, see you soon in the class room. Happy learning and have a great time.

Overview

Section 1: Course Introduction and Table of Contents

Lecture 1 Course Introduction and Table of Contents

Section 2: Introduction to Generative AI, Machine Learning and Deep Learning

Lecture 2 Generative AI vs Discriminative AI

Lecture 3 Introduction to Popular Generative AI Models

Lecture 4 Introduction to AI and Machine Learning

Lecture 5 Introduction to Deep learning and Neural Networks

Section 3: Setting up Computer

Lecture 6 Setting up Computer - Installing Anaconda

Section 4: Python Programming Basics

Lecture 7 Python Basics - Assignment

Lecture 8 Python Basics - Flow Control - Part 1

Lecture 9 Python Basics - Flow Control - Part 2

Lecture 10 Python Basics - List and Tuples

Lecture 11 Python Basics - Dictionary and Functions - part 1

Lecture 12 Python Basics - Dictionary and Functions - part 2

Section 5: Basic Python ML Library Basics: Numpy, Pandas & Matplotlib

Lecture 13 Numpy Basics - Part 1

Lecture 14 Numpy Basics - Part 2

Lecture 15 Matplotlib Basics - part 1

Lecture 16 Matplotlib Basics - part 2

Lecture 17 Pandas Basics - Part 1

Lecture 18 Pandas Basics - Part 2

Section 6: Deep Learning and Convolutional Neural Networks

Lecture 19 Installing Deep Learning Libraries

Lecture 20 Basic Structure of Artificial Neuron and Neural Network

Lecture 21 Activation Functions Introduction

Lecture 22 Popular Types of Activation Functions

Lecture 23 Popular Types of Loss Functions

Lecture 24 Popular Optimizers

Lecture 25 Popular Neural Network Types

Lecture 26 King County House Sales Regression Model - Step 1 Fetch and Load Dataset

Lecture 27 Step 2 and 3 EDA and Data Prepration - Part 1

Lecture 28 Step 2 and 3 EDA and Data Prepration - Part 2

Lecture 29 Step 4 Defining the Keras Model - Part 1

Lecture 30 Step 4 Defining the Keras Model - Part 2

Lecture 31 Step 5 and 6 Compile and Fit Model

Lecture 32 Step 7 Visualize Training and Metrics

Lecture 33 Step 8 Prediction Using the Model

Lecture 34 Heart Disease Binary Classification Model - Introduction

Lecture 35 Step 1 - Fetch and Load Data

Lecture 36 Step 2 and 3 - EDA and Data Preparation - Part 1

Lecture 37 Step 2 and 3 - EDA and Data Preparation - Part 2

Lecture 38 Step 4 - Defining the model

Lecture 39 Step 5 - Compile Fit and Plot the Model

Lecture 40 Step 5 - Predicting Heart Disease using Model

Lecture 41 Step 6 - Testing and Evaluating Heart Disease Model - Part 1

Lecture 42 Step 6 - Testing and Evaluating Heart Disease Model - Part 2

Lecture 43 Redwine Quality MultiClass Classification Model - Introduction

Lecture 44 Step1 - Fetch and Load Data

Lecture 45 Step 2 - EDA and Data Visualization

Lecture 46 Step 3 - Defining the Model

Lecture 47 Step 4 - Compile Fit and Plot the Model

Lecture 48 Step 5 - Predicting Wine Quality using Model

Lecture 49 Serialize and Save Trained Model for Later Usage

Lecture 50 Digital Image Basics

Lecture 51 Basic Image Processing using Keras Functions - Part 1

Lecture 52 Basic Image Processing using Keras Functions - Part 2

Lecture 53 Basic Image Processing using Keras Functions - Part 3

Lecture 54 Keras Single Image Augmentation - Part 1

Lecture 55 Keras Single Image Augmentation - Part 2

Lecture 56 Keras Directory Image Augmentation

Lecture 57 Keras Data Frame Augmentation

Lecture 58 CNN Basics

Lecture 59 Stride Padding and Flattening Concepts of CNN

Lecture 60 Flowers CNN Image Classification Model - Fetch Load and Prepare Data

Lecture 61 Flowers Classification CNN - Create Test and Train Folders

Lecture 62 Flowers Classification CNN - Defining the Model - Part 1

Lecture 63 Flowers Classification CNN - Defining the Model - Part 2

Lecture 64 Flowers Classification CNN - Defining the Model - Part 3

Lecture 65 Flowers Classification CNN - Training and Visualization

Lecture 66 Flowers Classification CNN - Save Model for Later Use

Lecture 67 Flowers Classification CNN - Load Saved Model and Predict

Lecture 68 Flowers Classification CNN - Optimization Techniques - Introduction

Lecture 69 Flowers Classification CNN - Dropout Regularization

Lecture 70 Flowers Classification CNN - Padding and Filter Optimization

Lecture 71 Flowers Classification CNN - Augmentation Optimization

Lecture 72 Hyper Parameter Tuning - Part 1

Lecture 73 Hyper Parameter Tuning - Part 2

Lecture 74 Transfer Learning using Pretrained Models - VGG Introduction

Lecture 75 VGG16 and VGG19 prediction- Part 1

Lecture 76 VGG16 and VGG19 prediction- Part 2

Lecture 77 ResNet50 Prediction

Lecture 78 VGG16 Transfer Learning Training Flowers Dataset - part 1

Lecture 79 VGG16 Transfer Learning Training Flowers Dataset - part 2

Lecture 80 VGG16 Transfer Learning Flower Prediction

Lecture 81 VGG16 Transfer Learning using Google Colab GPU - Preparing and Uploading Dataset

Lecture 82 VGG16 Transfer Learning using Google Colab GPU - Training and Prediction

Lecture 83 VGG19 Transfer Learning using Google Colab GPU - Training and Prediction

Lecture 84 ResNet50 Transfer Learning using Google Colab GPU - Training and Prediction

Section 7: Popular CNN and Generative Network Types

Lecture 85 Popular CNN and Generative AI Network Types

Section 8: Type 1: GAN - Generative Adversarial Networks

Lecture 86 Generative Adversarial Networks GAN Introduction

Lecture 87 Simple Transpose Convolution using a grayscale image - Part 1

Lecture 88 Simple Transpose Convolution using a grayscale image - Part 2

Lecture 89 Simple Transpose Convolution using a grayscale image - Part 3

Lecture 90 Generator and Discriminator Mechanism Explained

Lecture 91 A fully Connected Simple GAN using MNIST DataSet - Introduction

Lecture 92 Fully Connected GAN - Loading the Dataset

Lecture 93 Fully Connected GAN - Defining the Generator Function - Part 1

Lecture 94 Fully Connected GAN - Defining the Generator Function - Part 2

Lecture 95 Fully Connected GAN - Defining the Discriminator Function - Part 1

Lecture 96 Fully Connected GAN - Defining the Discriminator Function - Part 2

Lecture 97 Fully Connected GAN - Combining Generator and Discriminator Models

Lecture 98 Fully Connected GAN - Compiling Discriminator and Combined GAN Models

Lecture 99 Fully Connected GAN - Discriminator Training - Part 1

Lecture 100 Fully Connected GAN - Discriminator Training - Part 2

Lecture 101 Fully Connected GAN - Discriminator Training - Part 3

Lecture 102 Fully Connected GAN - Generator Training

Lecture 103 Fully Connected GAN - Saving Log at Each Interval

Lecture 104 Fully Connected GAN - Plot the log at intervals

Lecture 105 Fully Connected GAN - Display Generated Images - Part 1

Lecture 106 Fully Connected GAN - Display Generated Images - Part 2

Lecture 107 Saving the Trained Generator for Later Use

Lecture 108 Generating fake images using the saved GAN Model

Lecture 109 Fully Connected GAN vs Deep Convoluted GAN

Lecture 110 Deep Convolutional GAN - Loading the MNIST Hand Written Digits Dataset

Lecture 111 Deep Convolutional GAN - Defining the Generator Function - Part 1

Lecture 112 Deep Convolutional GAN - Defining the Generator Function - Part 2

Lecture 113 Deep Convolutional GAN - Defining the Discriminator Function

Lecture 114 Deep Convolutional GAN - Combining and Compiling the Model

Lecture 115 Deep Convolutional GAN - Training the Model

Lecture 116 Deep Convolutional GAN - Training the Model using Google Colab GPU

Lecture 117 Deep Convolutional GAN - Loading the Fashion MNIST Dataset

Lecture 118 Deep Convolutional GAN - Training the MNIST Fashion Model using Google Colab GPU

Lecture 119 Deep Convolutional GAN - Loading CIFAR-10 Dataset and Defining the Generator - 1

Lecture 120 Deep Convolutional GAN - Loading CIFAR-10 Dataset and Defining the Generator - 2

Lecture 121 Deep Convolutional GAN - Defining the Discriminator

Lecture 122 Deep Convolutional GAN CIFAR 10 - Training the Model

Lecture 123 Deep Convolutional GAN - Training the CIFAR10 Model using Google Colab GPU

Lecture 124 Vanilla GAN vs Conditional GAN

Lecture 125 Conditional GAN - Defining the Basic Generator Function

Lecture 126 Conditional GAN - Label Embedding for Generator - Part 1

Lecture 127 Conditional GAN - Label Embedding for Generator - Part 2

Lecture 128 Conditional GAN - Defining the Basic Discriminator Function

Lecture 129 Conditional GAN - Label Embedding for Discriminator

Lecture 130 Conditional GAN - Combining and Compiling the Model

Lecture 131 Conditional GAN - Training the Model - Part 1

Lecture 132 Conditional GAN - Training the Model - Part 2

Lecture 133 Conditional GAN - Display Generated Images

Lecture 134 Conditional GAN - Training the MNIST Model using Google Colab GPU

Lecture 135 Conditional GAN - Training the Fashion MNIST Model using Google Colab GPU

Section 9: Type 2: VAE - Variational Auto Encoders

Lecture 136 Introduction to Variational Auto Encoders - Part 1

Lecture 137 Introduction to Variational Auto Encoders - Part 2

Lecture 138 VAE for MNIST Digits - Importing Libraries

Lecture 139 Initializing VAE Class and Define Encoder

Lecture 140 Defining the Encoder Decoder functions

Lecture 141 The Reparametrization Trick

Lecture 142 Define the Reparametrization Function

Lecture 143 Define the Forward Pass Function

Lecture 144 Define the Loss Function

Lecture 145 Define Transform and Load Dataset

Lecture 146 Running the Training Epochs

Lecture 147 Generating Digit Images using the Trained Model

Lecture 148 Generating only Specific Digit using Conditional VAE

Section 10: Type 3: Autoregressive Models - Natural Language Processing Fundamentals

Lecture 149 Introduction to Autoregressive Models

Lecture 150 Natural Language Processing Tasks - Part 1

Lecture 151 Natural Language Processing Tasks - Part 2

Lecture 152 NLP Text Prediction

Section 11: Type 3: Autoregressive Models - Transformers and LLMs

Lecture 153 Introduction to Transformers

Lecture 154 Popular Transformer Models

Lecture 155 Implementing a PreTrained GPT2 Model

Lecture 156 Comparing GPT with Deepseek

Lecture 157 Implementing a Pretrained DeepseekR1 Model

Section 12: LLM Customization - Fine Tuning and RAG (Retrieval-Augmented Generation)

Lecture 158 LLM Customization - Fine Tuning vs RAG

Lecture 159 Fine Tuning GPT2 Model - Part 1

Lecture 160 Fine Tuning GPT2 Model - Part 2

Lecture 161 Fine Tuning GPT2 Model - Part 3

Lecture 162 RAG Introduction

Lecture 163 RAG Example Part 1

Lecture 164 RAG Example Part 2

Lecture 165 RAG Example Part 3

Section 13: Agentic AI Fundamentals

Lecture 166 Introduction to Agentic AI

Lecture 167 Creating a File Processing System using Agentic AI - Part 1

Lecture 168 Creating a File Processing System using Agentic AI - Part 2

Lecture 169 Creating a File Processing System using Agentic AI - Part 3

Lecture 170 Creating a File Processing System using Agentic AI - Part 4

Lecture 171 Creating a File Processing System using Agentic AI - Part 5

Section 14: Popular Agentic AI Protocols - MCP, ACP, A2A

Lecture 172 Popular Agentic AI Protocols

Lecture 173 Implementing MCP in our Agentic AI File Processor

Lecture 174 Introduction to ACP - Agent Communication Protocol

Lecture 175 ACP File Processing Part 1

Lecture 176 ACP File Processing Part 2

Lecture 177 ACP File Processing Part 3

Lecture 178 Introduction to A2A - Agent to Agent Protocol

Lecture 179 A2A File Processing

Section 15: AI Agent Frameworks

Lecture 180 Introduction to AI Agent Frameworks

Lecture 181 LangChain AI Agent Framework - Part 1.1

Lecture 182 LangChain AI Agent Framework - Part 1.2

Lecture 183 LangChain AI Agent Framework - Part 2.1

Lecture 184 LangChain AI Agent Framework - Part 2.2

Section 16: Generative AI: Diffusion Models

Lecture 185 Introduction to Diffusion Models and Stable Diffusion

Lecture 186 Image Generation using Pretrained Stable Diffusion Model

Section 17: SOURCE CODE DOWNLOAD

Lecture 187 Download Source Code and Datasets

Beginners who want to start their journey in Python programming, AI, and Generative AI. Students and professionals who want to build a strong foundation in Deep Learning before diving into advanced AI models. Developers and data enthusiasts looking to upskill in Generative AI, LLMs, and Agentic AI frameworks. Researchers, hobbyists, and innovators interested in building real-world AI projects and autonomous AI agents.