Elite Program in AI & Data Science

• NumPy & Linear Algebra – Work with arrays and core matrix operations used in ML.
• Pandas for Data Manipulation – Clean, transform, and analyze real-world datasets using DataFrames.
• Data Preprocessing – Perform EDA, feature engineering, scaling, and handle missing values & outliers.
• Model Preparation Concepts – Apply train-test split, cross-validation, and bias-variance concepts.
• Data Visualization – Build insights using Matplotlib and Seaborn (bar, scatter, regression plots).

Understand how to use Python for Data Science, including working with APIs and building basic APIs.

• SQL Fundamentals – Learn SQL basics, ACID properties, normalization, tables, joins, and variables.
• Advanced SQL – Work with functions, subqueries, views, nested queries, and pattern matching.
• UDFs & Stored Procedures – Build user-defined functions and stored procedures for real-world use cases.
• Data Handling & Optimization – Perform grouping, sorting, indexing, and optimize queries for performance.

• Core ML Concepts – Learn supervised and unsupervised learning used in real-world applications.
• Regression & Classification – Build and optimize models like Linear Regression, Logistic Regression, Decision Trees, Random Forest, and KNN.
• Clustering Techniques – Apply clustering algorithms to group and analyze data patterns.
• Model Evaluation – Measure performance using accuracy, precision, recall, F1-score, and confusion matrix.
• Dimensionality Reduction – Use PCA and LDA to simplify high-dimensional data.
• Time Series Basics – Analyze trends and build models for future predictions.

• AI Chatbot with RAG – Build an intelligent chatbot using LLMs, embeddings, and vector databases for real-world applications.
• Customer Churn Prediction – Predict customer drop-offs using machine learning models used in SaaS and fintech companies.
• Recommendation System – Develop a Netflix/Amazon-style recommendation engine to personalize user experience.
• Fraud Detection System – Detect fraudulent transactions using classification models (real-world fintech use case).
• Sales Forecasting (Time Series) – Forecast demand and trends using historical data for business decision-making.
• Resume Screening AI (LLM-based) – Build an AI system to analyze and rank resumes using LLMs and NLP techniques.

• Transformers & Attention – Learn the architecture behind modern AI, including self-attention and encoder-decoder models.
• BERT & Language Models – Understand how models like BERT power text understanding and real-world NLP applications.
• GPT & Generative AI – Build applications using GPT models for content generation, automation, and AI assistants.
• Large Language Models (LLMs) – Learn how LLMs work, their use cases, and how they power modern AI systems.
• Generative Models (VAEs) – Create models for tasks like image generation and synthetic data creation.
• LangChain & LLM Applications – Develop real-world applications using LLMs, APIs, and end-to-end AI workflows.

• Prompt Engineering – Learn to design effective prompts, optimize outputs, and use tools to build reliable AI applications.
• Image-Based Applications – Create image generation and text-to-image workflows using Generative AI.
• Text-Based Applications – Build applications for summarization, content generation, and automation using LLMs.
• Audio-Based Applications – Develop text-to-audio and speech-based applications using Generative AI tools.

• Foundations & Architecture – Understand how AI agents are designed and operate in real-world systems.
• Core Components – Work with memory, tools, and RAG to build intelligent agents.
• Agent Development – Build use-case driven AI agents for automation and decision-making.
• Deployment – Deploy and scale AI agents for real-world applications.