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 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

AWS Academy Machine Learning Foundations introduces students to the concepts and terminology of Artificial Intelligence and Machine Learning. By the end of this course, students will be able to select and apply machine learning services to resolve business problems. They will also label, build, train, and deploy a custom machine learning model through a guided, hands-on approach.

Tools: Amazon SageMaker

This module deals with designing and implementing a data science solution on Microsoft Azure. The participants will learn to create and manage Azure and Databricks workspace, run and monitor models, automated MLs and tune hyperparameters, 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

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

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

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 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 Vision Services, Video Indexer, Language Understanding Service (LUIS), QnA Maker, etc.

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 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.