What is Data Science?
Overview of Generative AI
Applications and Use Cases
Evolution from Predictive to Generative Modeling
Tools & Platforms (Python, Jupyter, Hugging Face, OpenAI API, etc.)
Data Collection and Cleaning
Exploratory Data Analysis (EDA)
Feature Engineering
Data Visualization (Matplotlib, Seaborn, Plotly)
Supervised vs Unsupervised Learning
Regression & Classification Algorithms (Linear Regression, Decision Trees, SVMs)
Clustering Algorithms (K-means, DBSCAN)
Model Evaluation Metrics (Accuracy, F1 Score, ROC AUC)
Hyperparameter Tuning and Cross-Validation
Model Deployment Basics
What is Generative AI?
Generative Models Overview:
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Large Language Models (LLMs)
Ethics and Risks of Generative AI
Prompt Engineering Basics
Natural Language Processing Refresher
Transformers and LLMs (BERT, GPT, etc.)
Text Generation, Summarization, Q&A Systems
Prompt Engineering for LLMs
Fine-tuning LLMs using custom datasets
Use of APIs: OpenAI, Hugging Face Transformers
Image Generation with GANs (DCGAN, StyleGAN)
Data Augmentation using GANs
Image Captioning using Vision + NLP models
Diffusion Models (Intro)
Introduction to Multimodal AI
Combining Text, Image, and Audio data
Applications: Chatbots with Vision, Image-to-Text systems
Tools: CLIP, DALL·E, Flamingo, Gemini
Project 1: Build a Resume Analyzer using GPT and NLP
Project 2: Generate Synthetic Images using GANs for training a classifier
Project 3: Text-to-Image Web App using DALL·E or Stable Diffusion
Project 4: Fine-tune a GPT model on company support data for chatbot
Project 5: EDA + Predictive modeling + Report generation using LLM
Building APIs for LLMs
Deploying models using Streamlit, Gradio, Flask
Integrating Generative AI into web applications
Using LangChain or LlamaIndex for RAG (Retrieval-Augmented Generation)
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The fusion of Data Science and Generative AI is redefining how businesses make decisions, optimize processes, and unlock new value. While traditional data science relies heavily on statistical models and historical data, Generative AI introduces creativity, contextual understanding, and automation at scale. This synergy opens the door to smarter analytics, human-like insights, and more adaptive systems.
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