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Python for AI, Data Preprocessing, Numpy, Pandas, Scikit-learn, Classical ML (Regression, Classification, Clustering), Intro to Neural Networks
Neural Networks, CNNs, Transfer Learning, Object Detection (YOLO/Faster RCNN), Segmentation, OpenCV basics
Focus: Advanced NLP, computer vision, and multimedia analytics for AI-powered content understanding
Duration: Months 4 and 5
Affiliation Partner: iHub, IIT Patna
Module 1: Introduction to Multimedia Analytics
Overview of multimedia data types: speech, video, text
Role of AI in content understandingTools & frameworks (Python, OpenCV, NLTK, PyTorch/TensorFlow)
Module 2: Natural Language Processing (NLP) & Speech Recognition
Text preprocessing: tokenization, stemming, lemmatizationLanguage models & embeddings (Word2Vec, BERT, GPT)
Speech-to-text systems and ASR (Automatic Speech Recognition)Speech synthesis and voice modeling
Module 3: Video Analytics & Computer Vision
Video processing and frame extraction
Object detection and tracking in videos
Action recognition and scene understanding
Multimedia feature fusion (combining text, speech, and visual cues)
Module 4: Text Mining & Sentiment Analysis
Information retrieval and extraction from large text corpora
Named Entity Recognition (NER) and topic modeling
Sentiment classification for social media or customer reviews
Focus: GPS, satellite navigation, and precision positioning for location-based AI applications
Duration: Months 4 and 5
Affiliation Partner: iHub, IIT Tirupati
Module 1: Fundamentals of GPS & GNSS
Overview of GPS, GLONASS, Galileo, and BeiDou systems
Satellite orbits, signal propagation, and timing systems
GPS receivers and positioning algorithms
Module 2: Precision Navigation
Error sources in GPS: multipath, ionospheric, and tropospheric effects
Differential GPS (DGPS) and Real-Time Kinematic (RTK) techniques
Integration with inertial navigation systems (INS)
Module 3: Mapping & Geospatial Data Systems
Geographic Information Systems (GIS) basics
Spatial data analysis and visualization
Route optimization and geofencing applications
Module 4: Location Intelligence & AI Applications
Location-based recommendation systems
Predictive analytics for logistics, delivery, and smart cities
Multi-modal data fusion for location-aware AI applications
Focus: Multi-agent systems, distributed AI, and collaborative robotics
Duration: Months 4 and 5
Affiliation Partner: iHub, IIT Palakkad
Module 1: Multi-Agent Systems
Fundamentals of agents and multi-agent environments
Communication protocols and coordination strategies
Game theory and decision-making in multi-agent systems
Module 2: Distributed AI & Algorithms
Distributed machine learning and federated learning concepts
Consensus algorithms and decentralized optimization
Scalable AI solutions for networked agents
Module 3: Swarm Intelligence
Principles of swarm intelligence (ants, bees, birds)
Applications in robotics, logistics, and search operations
Optimization and task allocation in swarms
Module 4: Collaborative Robotics
Introduction to human-robot and robot-robot collaboration
Motion planning and path coordination
Multi-robot simulation and real-world deployment
In the final month of the program, learners take on a comprehensive capstone project that brings together all the skills they have gained in AI foundations and their chosen specialization. The capstone is designed to replicate real industry challenges, ensuring participants graduate with practical, demonstrable expertise.
Learners will:
1. Apply AI techniques from machine learning, deep learning, NLP, computer vision, and reinforcement learning.
2. Build end-to-end AI solutions tailored to their specialization domain (e.g., Healthcare AI, IoT, Human-AI Interaction, Media Analytics, Robotics).
3. Work under mentorship from IIT iHub faculty (online) while implementing projects at their offline training centers.
4. Showcase their work through a live demo, technical report, and viva evaluated by academic and industry experts.
The capstone ensures every learner leaves the program with a portfolio-ready AI project that proves their ability to tackle real-world problems with confidence.
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"The IIT faculty sessions were incredible. The practical approach and industry-relevant projects helped me transition into AI engineering seamlessly."
"The transition from student to AI engineer felt natural. The faculty pushed us with relevant projects, and that made all the difference."
"Learning GenAI through real projects gave me the confidence to design and deploy AI copilots at scale. It felt like stepping straight into the future of engineering."
"The specialization in Speech-to-Text transformed my understanding of conversational AI models. I gained skills that now power voice assistants and transcription tools at scale."
"The focus on GPS and location intelligence gave me practical expertise in geospatial data, navigation systems, and real-time tracking. Today, I apply those learnings to build smart mobility and logistics solutions."
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