GEN AI

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Why Generative AI is Important
in Data Science and AI

Generative AI creates new content—text, images, audio, video, and code—revolutionizing how humans and machines interact.

It powers large language models (LLMs) like GPT, Claude, LLaMA, which are used in AI assistants, chatbots, and productivity tools.

  • GenAI enables rapid prototyping, automation, and personalization, reducing manual effort in creative and cognitive tasks.

  • It enhances traditional AI pipelines by enabling data augmentation, simulation, and synthetic data generation.

  • In industries like healthcare and finance, GenAI helps in creating medical records, reports, and summarizing complex data.

  • It supports natural language interfaces for querying databases, coding, and automating workflows—key for next-gen AI apps.

  • GenAI models exhibit zero-shot and few-shot learning, making them versatile across a wide range of tasks without retraining.

  • It is a cornerstone for the rise of multimodal AI that combines text, image, speech, and video understanding.

  • GenAI unlocks creative AI—from AI-generated art to music, writing, and game design—blending tech and creativity.

  • Understanding GenAI is essential to build, fine-tune, and safely deploy modern AI systems in real-world products and services.

types-of-generative-AI
01

Module 1: Introduction to Generative AI

  1. What is Generative AI?

  2. Generative AI vs Discriminative AI

  3. Historical evolution: from GANs to LLMs

  4. Applications across industries (text, image, video, music)

  5. Challenges and ethical considerations

02

Module 2: Foundations of Generative Models

  1. Generative vs predictive modeling

  2. Probability and likelihood basics

  3. Maximum likelihood estimation (MLE)

  4. Bayesian inference (brief overview)

  5. Data distribution modeling

03

Module 3: Traditional Generative Models

  1. Gaussian Mixture Models (GMM)

  2. Hidden Markov Models (HMM)

  3. Naive Bayes as a generative classifier

04

Module 4: Deep Generative Models

  1. Introduction to neural network-based generation

  2. Autoencoders:

    1. Architecture and use cases

    2. Denoising Autoencoders

    3. Variational Autoencoders (VAEs)

  3. Generative Adversarial Networks (GANs):

    1. Generator vs Discriminator

    2. Vanilla GAN, DCGAN, Conditional GAN

    3. CycleGAN, StyleGAN (overview)

05

Module 5: Language Modeling & NLP Foundations

  1. What is a language model?

  2. N-gram models and limitations

  3. Introduction to embeddings:

    1. Word2Vec, GloVe, FastText

  4. Sequence modeling:

    1. RNNs, LSTM, GRU

06

Module 6: Transformer Architecture

  1. The Attention mechanism

  2. Scaled Dot-Product Attention

  3. Multi-Head Attention

  4. Encoder-Decoder structure

  5. Positional encoding

  6. Why Transformers replaced RNNs

07

Module 7: Large Language Models (LLMs)

  1. Understanding GPT architecture

    1. GPT-2 vs GPT-3 vs GPT-4

  2. Tokenization and vocabulary

  3. Pre-training vs fine-tuning

  4. Zero-shot, one-shot, few-shot learning

  5. Popular LLMs: OpenAI GPT, LLaMA, Mistral, Claude, Gemini

08

Module 8: Prompt Engineering

  1. What is a prompt?

  2. Prompt design principles

  3. Chain-of-thought prompting

  4. Role prompting and system messages

  5. Prompt tuning vs fine-tuning

  6. Tools: LangChain, LlamaIndex, Flowise (intro)

09

Module 9: Fine-Tuning and Adaptation

  1. Parameter-efficient tuning methods:

    1. LoRA, PEFT, QLoRA

  2. Supervised Fine-Tuning (SFT)

  3. Reinforcement Learning with Human Feedback (RLHF)

  4. Transfer learning in GenAI

10

Module 10: Evaluation of Generative Models

  1. Evaluation metrics for text:

    1. BLEU, ROUGE, METEOR, perplexity

  2. Evaluation metrics for images:

    1. FID, IS (Inception Score)

  3. Human evaluation

  4. Alignment and hallucination detection

11

Module 11: Tools and Frameworks

  1. OpenAI API (GPT-4, Assistants API)

  2. Hugging Face Transformers & Datasets

  3. LangChain, Vector databases (FAISS, Chroma)

  4. Gradio, Streamlit for GenAI apps

  5. Google Colab / Kaggle Notebooks

12

Module 12: Multimodal Generative AI

  1. What is multimodal learning?

  2. Image generation: DALL·E, Midjourney, Stable Diffusion

  3. Text-to-speech and speech-to-text: Whisper, ElevenLabs

  4. Vision-language models (CLIP, Flamingo, Gemini)

  5. Video generation (intro only)

13

Module 13: Responsible and Ethical GenAI

  1. Hallucination and misinformation risks

  2. Bias and fairness in generation

  3. Safety and content filtering

  4. Copyright, plagiarism, deepfakes

  5. Governance and regulations (EU AI Act, OpenAI guidelines)

14

Module 14: GenAI Applications & Projects

  1. AI-powered chatbots and copilots

  2. Summarization and content rewriting

  3. Text-to-image generation app

  4. Code generation and debugging

  5. Resume, email, or blog writing assistants

  6. Project: Build a mini AI assistant using OpenAI API + LangChain

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