Learn AI and Data Science with a Low-Code Approach

In this module, you will delve into the critical ethical considerations that underpin the entire AI lifecycle through practical insights and real-world examples. Introduction to AI Lifecycle Introduction to Bias and Its Examples Introduction to Causality and Privacy Interconnections and Domains Interdependency and Feedback in AI Systems

This module is designed to help you get the most out of the program. We begin an introduction to foundational topics in Python programming, statistics, the Data Science lifecycle, and the evolution of AI and Generative AI. This module is designed to prepare all learners, regardless of prior experience, to confidently engage with the comprehensive curriculum ahead. Introduction to the World of Data Introduction to Python Introduction to Generative AI Applications of Data Science and AI Data Science Lifecycle Mathematics and Statistics behind DS and AI History of DS and AI

In this module, you will learn hypothesis testing, dimensionality reduction, network analysis, and various clustering algorithms with practical applications. Hypothesis testing and practical applications Dimensionality reduction using PCA and t-SNE Network Analysis Different types of clustering algorithms

In this module, you will build foundational machine learning models and understand their evaluation. Maximum Likelihood, Bayesian Estimators & formulation Linear Regression & Assumptions Cross-validation & Bootstrapping Classification using Logistic Regression & KNN) Gaussian Models

In this module, you will build foundational machine learning models and understand their evaluation. Maximum Likelihood, Bayesian Estimators & formulation Linear Regression & Assumptions Cross-validation & Bootstrapping Classification using Logistic Regression & KNN) Gaussian Models

A dedicated break week to consolidate learning and catch up on pending coursework.

In this module, you will apply real-world techniques in classification, ensemble learning, and forecasting. Introduction to Decision Tree Entropy & Information Gain Ensemble Learning - Bagging, Bootstrapping, and Random Forests Time Series Forecasting

In this module, you will explore neural networks and their applications in computer vision and language processing. Introduction to Deep Learning Filters/Convolutions, Pooling, and Max-Pooling Architecture of CNN Transfer Learning and Augmentation Encoder Decoder Architecture Token-based Processing, Attention Mechanism & Positional Encodings

In this module, you will design intelligent systems for personalization using a variety of recommendation techniques. Introduction to the Recommendations Content-Based Recommendation Systems Collaborative Filtering & Singular Value Thresholding Matrix Estimation Meets Content-Based Matrix Estimation Over Time

In this module, you will work independently on a hands-on project that allows you to apply program concepts to a domain of your choice.

In this module, you will understand the architecture, evolution, and foundations of Generative AI. Origins of Generating New Data Generative AI as a Matrix Estimation Problem LLM as a Probabilistic Model for Sequence Completion Prompt Engineering

In this module, you will learn how Generative AI and Agentic AI can drive business transformation. Natural Language Tasks with Generative AI Summarization, Classification and Generation Retrieval Augmented Generation (RAG) Agentic AI

For your Capstone Project, you will solve a real-world business challenge using techniques from across the program. Projects are guided and evaluated by mentors and reviewed by industry experts.