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Post Graduate Program AI & Machine Learning

Duration: 1 Yr / 40 Weeks / 220 hrs

Industry Acceptance

This intensive 12-month program, designed for graduates of any discipline, provides a robust foundation in AI and ML concepts, equipping you with the skills and knowledge to thrive in this dynamic field. Whether you're a tech enthusiast, aspiring data scientist, or looking to pivot your career, this program is your bridge to a future filled with possibilities.

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Registration Fees

₹ 1500

non-refundable

Course Fees

₹ 275,000

Flexible EMI options available.

Batch Start

Aug, 2025

Duration

1 Yr / 40 Weeks / 220 hrs

Program Structure

Eligibility: Bachelor's degree from a recognized institution , with Mathematics as a subject with at least 50% aggregate marks and 1 year of work experience (Preferably)

Duration: 1 Yr / 40 Weeks / 220 hrs

Batch Start Date: Aug, 2025

Batch Size: 25-30

Courses and Certification Conduct Policy:

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Program Structure:

Module 1: Programming Bootcamp For Non-Programmers

Ever dream of building AI? This intensive 3-week bootcamp program equips you with the Python superpowers needed to unlock the world of Artificial Intelligence. It would serve as a training module for learners with limited or no programming exposures. It enables new learner to be at par with those learners who have prior programming knowledge before the PGPAI commences. This is an optional but open to all module to create a strong foundation of programming knowledge necessary to succeed as an AI/ML professional.

Module 2: FOUNDATIONS
Introduction to Python +

MODULE 1

  • Python Basics
  • Python Functions and Packages
  • Working with Data Structures, Arrays, Vectors & Data Frames
  • Jupyter Notebook – Installation & Function
  • Pandas, NumPy, Matplotlib, Seaborn
SELF PACED MODULE
  • EDA and Data Processing
  • Data Types
  • Dispersion & Skewness
  • Uni & Multivariate Analysis
  • Data Imputation
  • Identifying and Normalizing Outliers
Applied Statistics +

MODULE 2

  • Descriptive Statistics
  • Probability & Conditional Probability
  • Hypothesis Testing
  • Inferential Statistics
  • Probability Distributions
Introduction to SQL +

MODULE 3

  • Introduction to DBMS
  • ER Diagram
  • Schema Design
  • Key Constraints and Basics of Normalization
  • Joins
  • Subqueries Involving Joins and Aggregations
  • Sorting
  • Independent Subqueries
  • Correlated Subqueries
  • Analytic Functions
  • Set Operations
  • Grouping and Filtering
Module 3: MACHINE LEARNING
Supervised Learning +

MODULE 1

  • Linear Regression
  • Multiple Variable Linear Regression
  • Logistic Regression
  • Naïve Bayes Classifiers
  • K-NN Classification
  • Support Vector Machines
Ensemble Techniques +

MODULE 2

  • Decision Trees
  • Bagging
  • Random Forests
  • Boosting
Unsupervised Learning +

MODULE 3

  • K-means Clustering
  • Hierarchical Clustering
  • Dimension Reduction - PCA
Featurisation, Model Selection & Tuning +

MODULE 4

  • Feature Engineering
  • Model Selection and Tuning
  • Model Performance Measures
  • Regularising Linear Models
  • ML Pipeline
  • Bootstrap Sampling
  • Grid Search CV
  • Randomized Search CV
  • K Fold Cross Validation
Module 4: ARTIFICIAL INTELLIGENCE
Introduction to Neural Networks and Deep Learning +

MODULE 1

  • Introduction to Perceptron & Neural Networks
  • Activation and Loss Functions
  • Gradient Descent
  • TensorFlow & Keras for Neural Networks
  • Hyper Parameter Tuning
Computer Vision +

MODULE 2

  • Introduction to Convolutional Neural Networks
  • Introduction to Images
  • Convolution, Pooling, Padding & its Mechanisms
  • Forward Propagation & Backpropagation for CNNs
  • CNN Architectures like AlexNet, VGGNet, InceptionNet & ResNet
  • Transfer Learning
  • Object Detection
  • YOLO, R-CNN, SSD
  • Semantic Segmentation
  • U-Net
  • Face Recognition using Siamese Networks
  • Instance Segmentation
Natural Language Processing (NLP) +

MODULE 3

  • Introduction to NLP
  • Stop Words
  • Tokenization
  • Stemming and Lemmatization
  • Bag of Words Model
  • Word Vectorizer
  • TF-IDF
  • POS Tagging
  • Named Entity Recognition
  • Introduction to Sequential Data
  • RNNs and Its Mechanisms
  • Vanishing & Exploding Gradients in RNNs
  • LSTMs – Long Short-Term Memory
  • GRUs – Gated Recurrent Unit
  • LSTMs Applications
  • Time Series Analysis
  • LSTMs with Attention Mechanism
  • Neural Machine Translation
  • Advanced Language Models: Transformers, BERT, XLNet
SELF-PACED MODULE

Introduction to Reinforcement Learning (RL)

  • RL Framework
  • Component of RL Framework
  • Examples of RL Systems
  • Types of RL Systems
  • Q-Learning
SELF-PACED MODULE

Introduction to GANs (Generative Adversarial Networks)

  • Generative Networks
  • Adversarial Networks
  • How do GANs Work?
  • DCGANs – Deep Convolution GANs
  • Applications of GANs
ADDITIONAL MODULES
  • EDA
  • Time Series Forecasting
  • Pre Work for Deep Learning
  • Model Deployment
  • Visualization using TensorBoard
  • GANs (Generative Adversarial Networks)
  • Reinforcement Learning
  • Recommendation Systems
Module 5: Self Paced Modules
Demystifying ChatGPT and Its Applications +
  • Perceptron and MLP
  • Backpropagation
  • Activation & Loss Functions
  • Overfitting & Regularization
ChatGPT: The Development Stack +
  • Mathematical Fundamentals for Generative AI
  • VAEs: First Generative Neural Networks
  • GANs: Photorealistic Image Generation
  • Conditional GANs and Stable Diffusion
  • Transformer Models: Generative AI for Natural Language
  • ChatGPT: Conversational Generative AI
  • Hands-On ChatGPT Prototype Creation
  • Next Steps for Further Learning and Understanding
Module 6: INTRODUCTION TO GENERATIVE AI AND PROMPT ENGINEERING
Introduction to Generative AI +
  • AI vs ML vs DL vs GenAI
  • Supervised vs Unsupervised Learning
  • Discriminative vs Generative AI
  • A Brief Timeline of GenAI
  • Basics of Generative Models
  • Large Language Models
  • Word Vectors
  • Attention Mechanism
  • Business Applications of ML, DL and GenAI
  • Hands-on Bing Images and ChatGPT
Prompt Engineering 101 +
  • What is a Prompt?
  • What is Prompt Engineering?
  • Why is Prompt Engineering Significant?
  • How are Outputs from LLMs Guided by Prompts?
  • Limitations and Challenges with LLMs
  • Broad Strategies for Prompt Design
    • Template Based Prompts
    • Fill in the Blanks Prompts
    • Multiple Choice Prompts
    • Instructional Prompts
    • Iterative Prompts
    • Ethically Aware Prompt
  • Best Practices for Effective Prompt Design

Projects

Project 1

Identify potential customers with higher probability to churn using ensemble prediction model

A telecom company wants to use their historical customer data to predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.

Project 2

Cluster vehicles by fuel consumption attributes and train a regression model

The purpose is to classify a given vehicle as one of three types of vehicles, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.

Project 3

Create automation using computer vision to impute dynamic bounding boxes for vehicles

City X's traffic department wants to understand the traffic density on roads during busy hours to efficiently program their traffic lights.

Project 4

Implement an image classification neural network to classify Street House view numbers

Recognizing multi-digit numbers in photographs captured at street level is an important component of modern-day map making. A classic example is Google’s Street View imagery composed of hundreds of millions of geo-located 360-degree panoramic images.

Project 5

Predict patient condition based on test results

This project has two parts. In the first part, we predict the condition of the patient based on biomechanical test results. In part two, we design a supervised learning prediction model for targeted marketing for a bank's digital marketing campaign.

Project 6

Build a recommendation system for mobile phones using popularity and collaborative filtering

India is the second largest market globally for smartphones after China. About 134 million smartphones were sold across India.

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