Join us as we dive deep into the fundamental concepts of machine learning. Here's what you can expect:

Core Topics Covered:

• Supervised Learning: Understand how algorithms use labeled training data to predict and decide, all without explicit programming. We'll discuss creating labeled data, prediction methods, and real-world applications such as image classification and sentiment analysis.
• Unsupervised Learning: Get to know how systems learn from unlabeled data to spot patterns and relationships. We'll cover when and why to use this approach over supervised learning, introducing techniques like clustering. Applications? Think customer segmentation and anomaly detection.
• Reinforcement Learning: Delve into how agents interact with their surroundings to make decisions. It's all about maximizing rewards over time. We'll touch on the balance of exploring new options versus exploiting known paths, and discuss use cases like robotics and recommendation systems.

Additionally, we'll briefly discuss hybrid approaches, like semi-supervised learning, which combine elements of the primary types.

Who Should Attend?

• Beginners considering AI as a potential career.
• Those keen on understanding the foundational concepts of machine learning.

Pre-requisites:

• A basic grasp of programming.
• Understanding the digital storage of images.
• Comfort with concepts like pattern probabilities and iterative problem-solving.