AI-powered Predictive Analytics with Neural Networks

SKILLS: AI Solution Design | Predictive Decision-Making | Business Intelligence

Course Fee - ₹ 2000

Registration Closed

Overview

AI Powered Predictive Analytics with Neural Networks is an industry-oriented Artificial Intelligence and Predictive Analytics program designed and delivered by industry practitioners with extensive real-world implementation experience, along with experienced academicians possessing strong research and conceptual depth.
The course seamlessly bridges academic theory with practical, real-world AI applications, enabling learners to design, build, interpret, and deploy predictive models across diverse business and public-sector environments. This program equips learners with future-ready AI, Machine Learning, Deep Learning, and Neural Network skills that are increasingly demanded across industries such as finance, consulting, marketing, operations, HR, IT, and Government & Public Sector (GPS).

START DATE

Jul 01 2026

END DATE

Oct 15 2026

Delivery Mode

Online

Duration

15 Hours

Type

Theory

Course Fee

₹ 2000

Credentials and Certification

1 Credit

Course Category Micro-Credentials (Advanced)

TARGET AUDIENCE UG and PG Students, rnScholars and Professionals

Digital Credential

indicating verified micro-credential completion.

RECOMMENDED FOR

Students exploring industry-relevent skills

Professionals planning to upskill and reskill

Faculty building digital deployment literacy

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Course Outcomes

Our curriculum matches modern standard practices to provide exceptional training milestones.

AI and Machine Learning Fundamentals

Understand the core concepts of Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks for predictive analytics applications.

CORE KNOWLEDGE

Predictive Model Development

Develop end-to-end predictive models using real-world datasets to solve forecasting, classification, and business decision-making challenges effectively.

PRACTICAL LEARNING

Neural Network Applications

Build and evaluate neural network models for image classification, text analytics, anomaly detection, and intelligent pattern recognition tasks.

ADVANCED AI

Forecasting and Risk Analytics

Apply predictive analytics techniques to forecast outcomes, assess risks, and generate actionable insights for data-driven business decisions.

BUSINESS ANALYTICS

Model Evaluation and Deployment

Design, evaluate, and deploy scalable AI models by applying performance metrics and industry best practices for reliable implementation.

INDUSTRY READINESS

Ethical and Responsible AI

Apply ethical AI principles to develop transparent, responsible, and trustworthy predictive analytics solutions for real-world business applications.

RESPONSIBLE AI

ELIGIBILITY CRITERIA

UG students of Commerce, Management, Economics, Finance, Data Analytics, or related disciplines. Postgraduate students and research scholars seeking analytical decision-making skills. Working professionals, managers, and executives across industries. Aspiring data analysts and decision-makers interested in Excel- and AI-based analytics.

Course Instructor

Expert guidance from acclaimed industry professional leaders.

Dr Bidisha Sarkar

Assistant Professor

Dr. Bidisha Sarkar has seven years of experience which includes academic and industry. Her area of expertise is the energy sector. Dr. Sarkar had been a resource person for several FDPs and recipient of multiple Best Paper Awards. Her software proficiency includes Eviews, Gretl; SPSS, JASP; R, Python; NVIVO; Dedoose; VOSviewer; JAMOVI. She is serving as the Campus Coordinator of the Learning and Development Cell.

Program Overview

Academic Curriculum

A meticulous, guided learning path engineered to transform your cloud engineering expertise.

Chapter 1

Understand and Apply AI, Machine Learning, Deep Learning, and Neural Network Ecosystem and Concepts Introduction to Neural Network & 5 Vs of Big Data , Understanding the 6 Step Flow of AI Ecosystem - From Data to use Cases, Application of the 6 Step Flow of AI Ecosystem across sectors, Prerequisites to implement Neural Network models across sectors, Mitigating Data Integration Challenges for designing & implementing Neural Networks, Mitigating Data Quality Challenges for designing & implementing Neural Networks, Inside the Brain of AI: Demystifying Neural Network Architecture, Decoding Deep Learning: A Deep Dive into Neural Network through Simulation, Applications of different types of Neural Network based on data type- images , texts, videos,etc.

Chapter 2

Design and implement end-to-end AI solutions leveraging ML, Neural Network,Deep Learning techniques Open Source Technologies for Neural Network, CoTS Technologies for Neural Network & Comparison between the two approaches, Installation and basic usability of Orange, Exploratory Data Analysis (EDA), Logistic Regression, EDA on Logistic Regression, Tree Based Models, Entire AI Flow including Neural Network & Other Models, Credit Scoring, Image Analytics, Text Analytics

Chapter 3

Time Series Forecasting Time Series Analysis, Stationarity Test, Time Series Data Collection Process and Data Import using Orange Software, Understanding Data using Line Chart, Stationarity Test using Correlogram, Theory of ARMA and ARIMA, ARMA and ARIMA Test and Model Evaluation using Orange Software, Model Evaluation (Theory) and Analysis.