Foundational course in Neural Networks, Data Visualization, Cloud and Generative AI

Module of Advance Course in Embedded System Design with AI

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Course Objective

The Neural Networks, Data Visualization, Cloud and Generative AI course aims to equip learners with advanced knowledge and practical skills in deep learning, cloud computing, data visualization, and generative artificial intelligence. The course covers the design and implementation of neural network models, interactive data visualization, cloud-based AI deployment, and generative AI techniques for developing intelligent applications. Through hands-on projects and industry-relevant case studies, learners will gain experience in building, training, and deploying AI models using modern frameworks and cloud platforms while creating meaningful visualizations to support data-driven decision-making. Upon completion, participants will be prepared to develop scalable AI solutions for applications in healthcare, finance, manufacturing, robotics, smart cities, natural language processing, computer vision, and other intelligent systems.

Course Structure

The course is designed as an 8-credit program, with a total duration of 180 hours. (Theory 60 and Practical 120) Classes will be conducted six days a week (Monday to Saturday). Each day will comprise 2 hours of theory lectures followed by 2 hours of practical laboratory sessions, providing learners with a balanced combination of conceptual knowledge and hands-on experience.

Course Eligibility

Second year B.Sc. (Maths and Stat)/ B. Com (Maths) /BCA/BCS, 2 nd Year B.E (Any stream) or Equivalent

Prerequisite

The candidate must have a fundamental understanding of machine learning, statistics, linear algebra, probability, understanding of data structures, algorithms, and databases (SQL).

Course Content

Deep Neural Network Introduction to Neural Networks, Feedforward Neural Networks, Backpropagation, Activation Functions, Loss Functions, Optimisation Algorithms, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short- Term Memory Networks (LSTMs), Autoencoders, Generative Adversarial Networks (GANs), Deep Reinforcement Learning, Transfer Learning, Neural Network Regularization Techniques, Hyperparameter Tuning Data Visualization using TABLEAU Introduction to Tableau, Tableau Interface and Features, Connecting to Data Sources, Data Preprocessing in Tableau, Creating Basic Charts (Bar, Line, Pie), Advanced Chart Types (Heat Maps, Tree Maps, Scatter Plots), Dashboard Creation, Interactive Dashboards, Using Filters, Groups, and Sets, Calculated Fields and Table Calculations, Parameters, Data Blending Fundamentals of Cloud Computing Introduction to Cloud Computing, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models (Public, Private, Hybrid), Virtualization Basics, Cloud Infrastructure Components, Cloud Security Principles, Networking in the Cloud, Storage Options in the Cloud, Identity and Access Management (IAM), Cloud Migration Strategies, Monitoring and Management in the Cloud Generative AI and Prompt Engineering Introduction to Generative AI, Basics of Natural Language Processing (NLP), Introduction to Prompt Engineering, Pretrained Language Models (such as GPT, BERT), Text Generation Techniques, Fine-Tuning Language Models, Conditional Generation, Unconditional Generation, Sampling Strategies, Diversity and Control in Generation, Bias and Fairness in Generative AI

Admission and Selection Process

The application form is available on the website. Candidates must submit the completed application form and appear for the Common Entrance Test (CET), which will be conducted offline at our centre. The CET syllabus and assessment will be based on the specific course module selected by the candidate. Candidates who successfully complete the written assessment will be required to appear for a Viva Voce (oral interview) as part of the admission process. Final selection will be based on the candidate’s performance in both the CET and the Viva Voce.

Fee Structure

Course Fees: Rs.25,000/