ML 101

In my ML 101 talk, I covered the basics of Machine Learning, aiming to make the topic approachable to everyone in the audience. I started by explaining the core concepts of supervised, unsupervised, and reinforcement learning, along with their respective use cases. The audience gained insights into the importance of training data, feature engineering, and model evaluation. I illustrated the machine learning workflow step-by-step, from data collection and preprocessing to model training and deployment. To make the topic more engaging, I introduced the audience to Teachable Machine, a user-friendly web-based tool developed by Google. With Teachable Machine, I demonstrated how even beginners could create a simple solution using machine learning. The audience was thrilled to see how they could train a model with their own data and use it for classification tasks, such as recognizing objects or sounds. This hands-on demonstration helped solidify the concepts and showcased the power of machine learning in solving real-world problems.