AI & Data Science

Master end-to-end AI workflows and drive business impact with practical, hands-on training with 100% job support

Data engineering

Home

Why AI & Data Science Matters

In today’s data-driven world, the ability to extract insights from vast datasets and deploy machine learning models is a competitive advantage. Our AI & Data Science program equips your team with the skills to build, evaluate, and operationalize AI solutions that solve real business challenges.



Flexible learning: fully online and instructor-led offline sessions



Personalized learning paths based on AI-driven skill diagnostics



Hands-on labs and real-world capstone projects



Dedicated mentorship and expert code reviews



100% placement support including interview prep, role matching, and career guidance

Enroll Now

PROGRAM OVERVIEW

Over 7 months, learners progress through four core modules—Data Preparation, Model Building, MLOps, and Deployment—integrating live sessions, practical exercises, and AI-driven diagnostics to ensure mastery.

Join Now

AI Training Program | Full-Width Curriculum

The Complete AI & Machine Learning Training Program

Phase 1: Foundation

🎯 Goal: Build essential programming, math, and data handling skills.

Module 1: Python Programming Core

Variables, loops, functions, and modules
Advanced data structures (lists, dicts, sets)
Object-Oriented Programming (OOP) Basics

Module 2: Mathematics for AI

Linear algebra (vectors, matrices, operations)
Probability & statistics essentials
Calculus intuition (gradients, derivatives)

Module 3: Data Science Toolkit

NumPy for numerical operations
Pandas for data manipulation and analysis
Exploratory Data Analysis (EDA) with Matplotlib

Phase 2: Machine Learning

🎯 Goal: Understand and apply classical ML algorithms.

Module 4: Supervised Learning

Linear & Logistic Regression
Decision Trees, Random Forests, SVM
Model evaluation (Accuracy, F1, ROC)

Module 5: Unsupervised Learning

Clustering with K-Means & Hierarchical
Dimensionality Reduction with PCA & t-SNE

Module 6: Model Deployment Basics

Saving and loading models (Pickle, Joblib)
Creating a simple web API with Django

Project 1: Predictive Modeling

Build and deploy a predictive model (e.g., Credit Risk).

Phase 3: Deep Learning

🎯 Goal: Train neural networks and master DL frameworks.

Module 7: Neural Network Fundamentals

Perceptrons, backpropagation, and gradient descent
Activation & Loss functions

Module 8: Deep Learning Architectures

CNNs for Image Classification
RNNs & LSTMs for Sequential Data
Transfer Learning with Pretrained Models

Module 9: Natural Language Processing (NLP)

Text preprocessing and tokenization
Sentiment Analysis and modern Chatbots

Project 2: Deep Learning Application

Build an app for object detection or sentiment analysis.

Phase 4: Advanced AI

🎯 Goal: Bridge the gap between theory and industry standards.

Module 10: End-to-End AI Pipelines

Data collection, cleaning, and feature engineering
Model tuning, deployment, and monitoring

Module 11: Production-Ready Deployment

High-performance APIs with FastAPI
Containerization with Docker
Basic Cloud Deployment (AWS/GCP/Azure)

Module 12: AI Ethics & Fairness

Principles of Responsible AI
Identifying and mitigating bias in models

Project 3: Domain-Specific Application

Build a real-world app (e.g., Resume Screening).

Phase 5: Capstone & Career

🎯 Goal: Synthesize knowledge and prepare for the job market.

Module 13: Capstone Project (8 Weeks)

Choose a domain (Healthcare, Retail, HR, etc.)
Execute a complete project lifecycle from idea to deployment
Create a final presentation and professional documentation

Module 14: Interview & Resume Prep

Tackling common ML and Data Science interview questions
Building a powerful, project-based resume and GitHub profile
Mock interviews (HR + Technical rounds) with feedback

Ashutosh Dwivedi

PhD, IIT Kanpur • AI & Cybersecurity

Expert in Artificial Intelligence, Machine Learning, Computer Vision, Data Analytics and Embedded Systems. Co-author of “Digital Communication using MATLAB.”