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Generative AI and

Machine Learning

This course offers a comprehensive journey through the foundational principles of machine learning and the rapidly evolving field of generative artificial intelligence (AI). Designed for students, developers, and professionals eager to build intelligent systems, the course combines theoretical knowledge with hands-on experience in developing both predictive and generative models.

Starting with classical machine learning algorithms and neural networks, students will progress to deep learning architectures and cutting-edge generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Large Language Models (LLMs) like GPT and BERT.

Participants will explore real-world applications such as image synthesis, text generation, conversational AI, and AI-assisted design. By the end of the course, learners will be able to build, fine-tune, and deploy generative AI systems responsibly and effectively.

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💡 Key Topics

Machine Learning fundamentals: regression, classification, clustering

Neural Networks and Deep Learning with TensorFlow and PyTorch

Convolutional and Recurrent Neural Networks (CNNs, RNNs, LSTMs)

Introduction to Generative AI: concepts, types, use cases

Generative Adversarial Networks (GANs)

Diffusion Models for high-quality image generation

Transformers and Large Language Models (GPT, BERT, etc.)

Prompt engineering and fine-tuning with Hugging Face & OpenAI APIs

Ethics of AI: bias, fairness, misinformation, deepfakes

Model deployment using Streamlit, Gradio, or Hugging Face Spaces

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

📦 Tools & Frameworks

Python

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Hugging Face Transformers

OpenAi API

Stable Diffusion

DALL.E

Jupyter

Google Colab

Streamlit

📕Learning Outcomes

Understand and implement core ML and deep learning models

Build generative models for text, image, and multimedia creation

Use and fine-tune pre-trained LLMs and vision models

Design, evaluate, and deploy generative AI applications

Navigate the ethical and social challenges of generative AI

📕 Generative AI Content Outline

Module 1: Foundations of Machine Learning
  • What is AI, ML, and Generative AI?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Key ML Concepts: Features, Labels, Overfitting, Underfitting, Model Generalization
  • Introduction to Python for ML (NumPy, Pandas, Matplotlib)
  • Scikit-learn Basics and Workflow
  • Linear Regression and Logistic Regression
  • Decision Trees, Random Forests, Gradient Boosting (XGBoost)
  • Clustering: K-Means, DBSCAN
  • Dimensionality Reduction: PCA, t-SNE
  • Model Evaluation: Accuracy, Precision, Recall, F1-Score, ROC-AUC
  • Neural Network Basics: Perceptron, Activation Functions, Forward/Backward Propagation
  • Building Neural Networks with TensorFlow or PyTorch
  • Deep Learning Architectures
  • Fully Connected Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs), LSTMs, GRUs
  • Training, Optimizers (SGD, Adam), and Loss Functions
  • What is Generative AI? History, Evolution, Use Cases
  • Discriminative vs. Generative Models
  • Applications: Image Generation, Text Generation, Audio Synthesis, Style Transfer
  • GAN Architecture: Generator and Discriminator
  • Training GANs: Loss Functions, Challenges (Mode Collapse, Instability)
  • Variants: DCGAN, CycleGAN, StyleGAN
  • Practical Use Cases: Face Generation, Art Creation, Data Augmentation
  • Intuition Behind Diffusion Models
  • Denoising Diffusion Probabilistic Models (DDPMs)
  • Stable Diffusion Overview
  • Hands-on Image Generation with Open-Source Tools
  • Attention Mechanism and Self-Attention
  • Transformer Architecture (Encoder–Decoder)
  • Pretrained LLMs: GPT, BERT, T5, LLaMA
  • Fine-Tuning and Transfer Learning
  • Prompt Engineering: Best Practices and Use Cases
  • Text Generation and Summarization
  • Chatbots and Conversational AI
  • Image Generation (DALL·E, Stable Diffusion)
  • AI for Code (Codex, GitHub Copilot)
  • AI in Music, Voice, and Video Synthesis (basic overview)
  • Working with Hugging Face Transformers
  • Using OpenAI APIs (GPT, DALL·E)
  • Deploying AI Apps: Streamlit, Gradio, Flask
  • Ethical AI: Bias, Fairness, Deepfakes, Copyright
  • Responsible Use of Generative AI in Industry
  • Choose your own Generative AI project
  • Build a text-to-image generator
  • Create a custom chatbot
  • Generate music or art using AI
  • Train and fine-tune a GPT-style model
  • Deliverables: Model, Report, Presentation, (Optional) Deployed App
  • Languages: Python
  • ML/DL Libraries: Scikit-learn, TensorFlow, PyTorch
  • Generative AI Tools: Hugging Face, OpenAI API, Diffusers (Stable Diffusion)
  • Dev Platforms: Jupyter, Google Colab, GitHub, Streamlit/Gradio

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Ideal for fast-paced learning with daily live sessions and hands-on projects

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Industry Leading Placement

Whether you're aspiring to become a full-stack developer, looking to enhance your career, or build your own end-to-end applications, this

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