AI Machine Learning and Deep Learning

Day 1: Mastering Machine Learning and Deep Learning Basics

• Introduction to Machine Learning: Gain an understanding of ML fundamentals, key concepts, and the role of algorithms in data-driven predictions.
• Building Machine Learning Models: Learn how to create ML models, from data preparation and training to evaluation and optimisation.
• Advanced Machine Learning Techniques: Explore sophisticated techniques such as ensemble learning, feature engineering, and model interpretability.
• Introduction to Deep Learning: Transition to DL with an overview of neural networks and their applications in solving complex problems.

Day 2: Deep Dive into Deep Learning

• Convolutional Neural Networks (CNNs): Discover the power of CNNs for image recognition and computer vision tasks.
• Recurrent Neural Networks (RNNs): Understand RNNs and their applications in sequential data processing, including natural language processing.
• Advanced Deep Learning Techniques: Dive into cutting-edge topics like transformers, transfer learning, and generative models.
• Applications of Deep Learning: Explore real-world case studies showcasing DL applications across industries such as healthcare, finance, and automation.
• Future Trends and Ethical Considerations: Discuss the latest advancements in DL and address critical ethical challenges, including bias, fairness, and transparency.

By the end of the course, participants will have a solid foundation in machine learning and deep learning, enabling them to apply these technologies effectively and responsibly in their domains.