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In this module, the participants will learn the building blocks of intelligent systems. They will explore the best practices to harness the power of complex data structures like lists, sets, dictionaries, and tuples to store the data. They will also learn to design custom functions, write scripts, handle errors, and create and generate basic AI algorithms. They will be able to embed reasoning and thinking into programming. After completing this module, the participants will be able to manipulate the functions, source codes, and debug the Python programs, write fluent Python reasoning-based functions, and integrate programs and functions written in different languages/tools.

Tools: Python

This course provides knowledge and skills for understanding Linear Algebra fundamentals and applications in Machine Learning. Participants will work on a few in-classroom projects to get the firsthand experience of their skills. The learners will work on advanced matrix operations and supervised and unsupervised algorithms using linear algebra. Hands-on projects on customer segmentation, dimension reduction, and topic modeling are the highlights of the module.

Tools: Python

The course on Data Processing for AI will provide knowledge and skills related to SQL, for handling structured/tabular data. Participants will work with structured and unstructured data and its types. They will learn about collecting and accessing text data from different sources, implementing text cleaning, preprocessing and feature engineering in NLP, and advanced methods of feature engineering. At the end of the course, the participant will be equipped with knowledge, skill, and experience to handle structured, i.e., tabular data and unstructured data such as text, image, video, etc., in AI and Data Science Projects.

Tools: SQL, NoSQL, Python

This module aims to provide a strong foundation in applying statistical concepts and methods in real-world situations and domains related to analytics. The course intends to provide them with experience in communicating the results to non-statistical/business stakeholders and present their cases with appropriate business relevance. They will learn univariate, bivariate, and multivariate statistical techniques, setting and testing hypotheses, sampling, probability, and building statistical models.

Tools: Python, R

This module combines concepts, tools, and practical workshops to familiarize and build expertise in Machine Learning algorithms and model building processes. Practical skills in building supervised and unsupervised algorithms through industry-relevant case studies and datasets are the focus. The topics covered include approaches to ML, Data preparation for ML, Supervised learning, Linear models, Neural networks, Perceptron, Linear SVM, Non-linear models, Multi-layer perceptron, Decision trees, Random forests, Support vector machines, Bayesian modeling, Independent models, Naïve Bayesian modeling, and more.

Tools: Python

The objective of this module is to make the participants implement and apply state-of-the-art deep learning models in real-world problems. The participants will get in-depth understanding and hands-on exposure to applying deep learning methods to solve a variety of computer vision-based problems. The labs include building a deep neural network for a given problem, hyper-parameter tuning, and optimization to make sure it avoids overfitting, building a time sequence-based neural networks and solving the problems of forecasting, building CNN for image processing tasks, learn how and when to use Autoencoders and familiarize with the latest advances in deep learning.

Tools: Python

This course provides an introduction to some of the foundational ideas on which modern reinforcement learning is built, including Markov decision processes, value functions, Monte Carlo estimation, dynamic programming, temporal difference learning, eligibility traces, and function approximation. This course will develop an intuitive understanding of these concepts (taking the agent’s perspective) while also focusing on the mathematical theory of reinforcement learning. Programming assignments and projects will require implementing and testing complete decision-making systems.

Tools: Python, OpenAI Gym, Keras, Jupyter Toolkit

The objective of this course is to develop the skills to gain a basic understanding of neural network theory and fuzzy logic theory and introduce students to artificial neural networks and fuzzy logic theory, and introduce from an engineering perspective. The learner will work on fuzzy logic, fuzzy inference systems, probability theories, Bayesian probability-prior, conditional and posterior probabilities, Bayesian network, Gibbs sampler, inference in graphical models, reasoning with uncertainty, unsupervised learning – expectation maximization, supervised learning – Naïve Bayes, Full Bayes, genetic algorithms, genetic programming, and more.

Tools: Python, OpenAI Gym, Keras, Jupyter Toolkit

This course will focus on building end-to-end lifecycle of AI applications, from deployment to ongoing maintenance. The participants will learn best practices in integrating AI models into production environments, optimizing performance, and ensuring system reliability. The course will cover key aspects such as version control, scalability, and automated monitoring. Tools used include Docker for containerization, Kubernetes for orchestrating containerized applications, TensorFlow for deploying AI models, and Jenkins for continuous integration and continuous deployment (CI/CD). This course equips learners with the skills needed to manage robust, scalable, and efficient AI systems in real-world settings.

Tools: Python/Google Collab, AWS/Azure

The first-year Artificial Intelligence program will culminate in designing, preparing, and presenting a real-time capstone project on a live technical/business challenge. Each of the participants gets to work with a senior industry mentor for guidance spanning six weeks. The final certification is strictly based on the successful completion and submission of the capstone project with a favorable assessment from the panel of industry mentors. The objective is to develop your managerial and consulting capabilities by applying the lessons learned in the program to real-life AI challenges.

Tools: Full-stack

This course will explore the principles and techniques used in real-world computer vision systems and the research and development of new systems. The module will cover image processing fundamentals, basics and applications of digital image processing, intensity transformations and color image processing, image filtering, and advanced image processing methods, spatial image filtering, image features, feature matching, texture matching, image segmentation, clustering images, classification of images, video analytics, and more. At the end of this course, the participants will understand and master essential knowledge, theories, and methods in image processing and computer vision.

Tools: Python, Tensorflow, Keras

This course provides a unique opportunity for you to learn key components of text mining and analytics aided by real-world datasets and the text mining toolkit. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help the learners to be competent data scientists. After completing this course, participants will know all the basic concepts of natural language processing to be applied with text data and hands-on exposure to implementing NLP models to find insights from the text data.

Tools: Python

The objective of this course is to make the participants able to develop and use speech analytics tools that can indicate why the customers are calling, provide real-time analytics for agents, and even monitor conversions. At the end of this course, participants will be familiar with speech analytics methods and applications, along with hands-on knowledge of how to develop and use speech analytics tools and apply speech analytics methods to address real-world problems. The labs will include elements of speech systems, specular reflection, specularity, speech Input device, speech analysis, speech parameterization, speech processing, speech production, speech recognition, speech spectral envelope, speech synthesis, speech system, text-to-speech, and speech-to-text modeling, and more.

Tools: Python

This course focuses on the architecture, tools, and techniques used to collect, store, process, and analyze large-scale data. Learners will learn to design and implement data pipelines, optimize data workflows, and ensure data quality and integrity. The course covers distributed computing, data warehousing, and real-time processing, along with advanced analytics and machine learning. Key areas include Hadoop for distributed storage, Spark for large-scale data processing, Kafka for real-time data streaming, SQL for database management, and Python for data analysis. This course prepares learners to build robust big data solutions and derive actionable insights from complex datasets.

Tools: Hadoop, Spark, Kafka, NoSQL, SQL, Python

This course explores the creation of models capable of generating new, original content such as text, images, and music. Participants will learn about the principles of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. The course covers techniques for training and fine-tuning these models, as well as ethical considerations and real-world applications. Key tools include TensorFlow and PyTorch for building and training models, GPT for text generation, DALL-E for image creation, and Jupyter Notebooks for experimentation and visualization. This course equips the learners with the skills to develop and apply cutting-edge generative AI technologies in various domains.

Tools: TensorFlow, PyTorch, GPT, Python

The second-year AI program will culminate in designing, preparing, and presenting/publishing a real-time capstone project on a live technical/business challenge in AI. The scope and business applications of the project need to be larger than the first-year project. Designing and implementing, deploying, and demonstrating an end-to-end AI project with a reasonable monetary benefit need to be built. The program office will support with mentoring and report writing. The evaluation is based on viva-voce by an industry panel of experts.

Tools: Full Stack

This module deals with designing and implementing a data science solution on Microsoft Azure. The participants will learn to create and manage Azure and Data Bricks workspace, run and monitor models, automated MLs and tune hyper-parameters, and deploy and operationalize machine-learning solutions in the Azure cloud environment. The participants will go through the DP-100 to qualify as Microsoft Certified Azure Data Scientists.

Tools: Microsoft Azure Machine Learning Studio, Databricks

This is a Microsoft Azure-based module with certification. The learners will be able to design and deploy AI systems with Azure through this course. They will plan and manage an azure cognitive services solution, implement computer vision solutions, NLP solutions, knowledge solutions, and conversational AI solutions. After completing the certification exam, they will be designated as Azure AI Engineer Associate.

Tools: Microsoft Azure Cognitive Services, Custom Visions Services, Video Indexer, Language Understanding Service (LUIS), QnA Maker, etc.

This module credits can be earned by writing and publishing a research paper on the capstone project done in the earlier module. The participant needs to publish or present the paper in a peer-reviewed journal or reputed international conference. The participants will learn skills in research paper writing and publishing/presenting. The program office will provide extensive support on publications, which includes access to research articles and repositories, five-plus paid databases, writing boot camps, mentoring, publication, and patenting support.

Tools: Python.