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What you'll learn

Course Description: This six-week course provides a comprehensive immersion and smooth transition from Python, AI, ML, and DL to cutting-edge GenAI, essential for careers in science, engineering, management, and beyond. It offers vital skills to innovate and create solutions with AI technology, enhancing job market preparedness and competitiveness for students, faculties, and professionals alike. The course do not assume any pre-requisite knowledge of Python or AI or ML, although such previous exposure would be definitely helpful. Learning Outcome: At the end of this course the students will be able to • utilize basic concepts and terminology related to AI, ML, DL and GenAI for applications in science, engineering, and management • explore various deep learning and generative AI architectures including attention mechanisms • build real-world projects applying deep learning and generative AI models • develop and fine-tune Large Language Models (LLMs) for a specific application • gain insights into ethical considerations and best practices in AI development

  • Overview of artificial intelligence, machine learning, deep learning, and generative AI; Basic concepts and terminology; Machine Learning in Production (MLOps): project cycle, Tools, deployment options; Real-world applications and case studies; Python as a programming language; Data exploration using NumPy and Pandas; Data visualization using Matplotlib and Seaborn; Artificial Neural Network (ANN) for classification and regression
  • From ANN to Convolutional Neural Network (CNN); Hyperparameter tuning in CNN, Data augmentation; Pre-trained Networks, Transfer Learning: with and without fine tuning; Text Processing and time series analysis using Recurrent Neural Network (RNN); Implementations and real life applications
  • Time Series Analysis using LSTM; Object Detection using YOLO; U-Net for Image Segmentation; Auto-Encoders for Compression and Denoising; Implementations and real life applications
  • Generative Models: Variation Auto-Encoder (VAE), Generative and Adversarial Network (GAN); Attention Mechanisms; Self-attention and Transformer; Diffusion Models for Image Generation; Implementations and real life applications
  • Large Language Model, ChatGPT, DALL-E, Hugging Face and other tools; Prompt engineering; LLM Fine-tuning and Building Applications; Implementations and real life applications
  • Exploring GenAI for Daily Life: personal and professional (Coding, time and office management using GenAI Tools); Ethical AI; Implementations and real life applications

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Dr. Bhaveshkumar C. Dharmani
Professor

Dr. Bhaveshkumar C. Dharmani is a Professor and Head, Signal & Image Processing, School of Electronics & Electrical Engineering, Lovely Professional University, Punjab, India. He has done his full-time Ph.D. from Dhirubhai Ambani Institute of Information & Communication Technology (DAIICT), Gandhinagar, and held Visiting Scientist position for eight months at Interdisciplinary Statistical Research Unit (ISRU), Indian Statistical institute (ISI), Kolkata, India. His vast teaching and research experiences have inculcated in him multidisciplinary research ability that balances both mathematical rigour and technological vigour. His research area spans Statistical Signal Processing, Robust Statistical Divergences, Machine Learning, as well as Biomedical Signal and Image Processing. Currently, his research focus is on exploring Generative AI and the deployment of deep learning applications (MLOps) for healthcare.