Artificial Intelligence & Machine Learning
AI & Machine Learning Fundamentals
Build a strong understanding of Artificial Intelligence, Machine Learning, supervised and unsupervised learning techniques.
Data Analysis & Model Development
Learn to work with real-world data using Python, NumPy and Pandas, and build, train and evaluate machine learning models.
Computer Vision & Deep Learning
Explore neural networks, deep learning, OpenCV, image processing and object detection for intelligent applications.
Real-World AI Applications
Apply AI and Machine Learning concepts to practical projects, automation systems and real-world industry applications.
8
Months Program
32
Weeks Mastery Path
7 + 1
Months Training + Internship
3D Sim
Robotics Simulation
About the Program
PG Diploma in AI & Machine Learning
Learn with us. Build your future in AI.
The PG Diploma in AI & Machine Learning is an 8-month intensive, hands-on program designed to build industry-ready skills in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. The curriculum emphasises real-world projects and continuous practical learning to bridge the gap between theory and industry needs.
A key highlight of the program is its integration with advanced robotics simulation, enabling students to design, train and test AI-driven systems in realistic, physics-based 3D environments. With 7 months of expert-led training and a 1-month industry internship, this program prepares aspiring AI professionals to confidently solve real-world challenges.
Why Choose This Program?
100% Hands-On Learning
Learn by building real AI systems and applications through practical, project-based learning.
Future-Ready AI Skills
Build expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing and Computer Vision.
Advanced Robotics Simulation
Train and test AI-driven systems using advanced, realistic and physics-based 3D robotics simulation environments.
Real-World Projects
Build practical AI applications, predictive models, intelligent systems and other industry-focused projects from scratch.
1-Month Industry Internship
Gain real-world exposure and practical industry experience through a dedicated internship.
Fast-Track Career Growth
Become job-ready in 8 months with industry-focused training, practical projects and expert mentorship.
Learning Pathway
Roadmap & Syllabus
8 phases, taught with hands-on labs and projects.
Phase 1 · Weeks 1–4 Python Fundamentals for AI
- Python syntax, variables, data types and operators
- Conditional statements, loops and control flow
- Functions, modules and modular programming
- Lists, tuples, sets and dictionaries
- File handling and exception handling
- Object-Oriented Programming concepts
- Introduction to Python applications in AI
Phase 2 · Weeks 5–8 Advanced Python & Data Handling
- Advanced Python programming techniques
- NumPy arrays, indexing and mathematical operations
- Pandas for data manipulation and analysis
- Data cleaning and preprocessing
- Working with CSV, JSON and other data formats
- Data visualization using Matplotlib
- Practical data analysis projects
Phase 3 · Weeks 9–12 AI, Math & Data Science Foundations
- Introduction to Artificial Intelligence and Data Science
- Linear algebra fundamentals for AI
- Probability and statistics
- Vectors, matrices and mathematical operations
- Data distributions and statistical analysis
- Feature engineering and data preparation
- Understanding AI problem-solving workflows
Phase 4 · Weeks 13–16 Machine Learning Algorithms
- Introduction to Machine Learning workflows
- Supervised and unsupervised learning
- Regression algorithms
- Classification algorithms
- Clustering techniques
- Model training and evaluation
- Performance metrics and model optimisation
Phase 5 · Weeks 17–20 Deep Learning & Frameworks
- Introduction to Deep Learning
- Neural networks and network architecture
- Activation and loss functions
- Forward and backward propagation
- Convolutional Neural Networks (CNNs)
- Transfer learning techniques
- Training and deploying deep learning models
Phase 6 · Weeks 21–24 NLP, Computer Vision & Generative AI
- Natural Language Processing fundamentals
- Text processing and language models
- Computer Vision using OpenCV
- Image processing and object detection
- Introduction to Generative AI
- Working with modern AI tools and models
- Building practical AI-powered applications
Phase 7 · Weeks 25–28 Capstone Project
- Industry-oriented AI project development
- Problem identification and solution design
- Data collection and preprocessing
- Model development and training
- Testing, evaluation and optimisation
- AI application deployment
- Technical documentation and project presentation
Phase 8 · Weeks 29–32 Industry Internship
- Real-world industry exposure
- Working on practical AI and Machine Learning projects
- Applying AI concepts to real-world challenges
- Collaborative project development
- Model testing and performance improvement
- Portfolio development and project presentation
- Career preparation and industry readiness
Skills You Will Gain
Strong Python & Data Science
- Programming, data handling, visualization and Exploratory Data Analysis
Machine Learning Expertise
- Regression, classification and clustering models
Deep Learning
- CNNs, RNNs, LSTM and transfer learning using TensorFlow & PyTorch
NLP & Computer Vision
- Chatbots, sentiment analysis, image and video processing
Generative AI
- Introduction to Large Language Models and fine-tuning
MLOps Basics
- Model evaluation, APIs, CI/CD and responsible AI practices
Industry Exposure
- Real-world projects and hands-on industry internship experience
Career Paths
Core AI Engineering
- AI Engineer — Design and deploy intelligent systems
- Machine Learning Engineer — Build, train, optimize and deploy ML models
- Data Scientist — Analyze data and build predictive models
Applied & Generative AI
- GenAI Developer — LLMs, prompt engineering, RAG and AI agents
- AI Product Engineer — Build AI-powered features for scalable products
- Computer Vision Engineer — Develop image and video AI using CNNs and OpenCV
Specialist & Deployment Roles
- NLP Engineer — Build chatbots, sentiment analysis and language AI applications
- MLOps / AI Deployment Engineer — Deploy, monitor and manage AI models