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AI & Machine Learning

Training & Workshop on AI & Machine Learning

Industry Ready

Training & Workshop on AI & Machine Learning

Duration: 3 - 6 Days (36 Hrs)
Audience: Engineering Students & Researchers
Structure: 6 Applied AI Projects
Focus: PyTorch & Computer Vision
Training & Workshop on AI & Machine Learning

Key Highlights & Technical Capabilities

Deep Neural Networks (PyTorch)

Build, train, and backpropagate deep neural networks with GPU acceleration, transfer learning, and regularization.

Computer Vision & YOLOv8

Real-time object detection, segmentation, and facial recognition using OpenCV and state-of-the-art YOLO architectures.

Edge AI & Optimization

Quantize and optimize deep models using TensorRT and ONNX for real-time deployment on edge devices like Jetson and Raspberry Pi.

Program Overview

This comprehensive workshop equips engineering undergraduates and postgraduate researchers with real-world AI and deep learning competencies. Moving beyond theory, participants train and optimize state-of-the-art computer vision models, build transformer-based NLP workflows, and deploy models onto edge computing hardware.

Curriculum & Module Roadmap

01

Python for Data Science & Mathematical Foundations

Vectorized computing with NumPy, data manipulation with Pandas, statistical distributions, gradient descent, and loss optimization.

02

Supervised & Unsupervised Machine Learning Algorithms

Regression models, classification algorithms, Random Forests, XGBoost, clustering techniques, and model evaluation metrics.

03

Deep Neural Networks & PyTorch Frameworks

Multi-layer perceptrons, backpropagation, activation functions, regularization, and training deep neural networks with GPU acceleration.

04

Computer Vision & Object Detection with OpenCV & YOLO

Image processing fundamentals, Convolutional Neural Networks (CNNs), transfer learning, and real-time object detection with YOLOv8.

05

Natural Language Processing & Transformer Models

Text embeddings, tokenization, transformer architectures, HuggingFace pipeline integration, and fine-tuning LLMs.

06

Model Optimization, Edge Deployment & Hackathon

Quantization, ONNX / TensorRT conversion, edge inferencing on Raspberry Pi / Jetson, and end-to-end project presentation.

Institutional & Student Deliverables

  • Interactive Jupyter notebook lab environments with GPU cloud acceleration support.
  • Curated datasets and production-grade starter codebases across vision and NLP domains.
  • Verified AI/ML Practitioner Certification co-branded with AxiLearn.
  • Post-workshop mentorship for academic publications and competitive hackathons.
Partnership Opportunities

Deploy This Program at Your Institution

Book an on-campus demonstration, request turnkey lab setup proposals, or customize syllabi for your department.

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