As machine learning continues to evolve and integrate with embedded systems, TinyML emerges at the intersection, offering the potential to run machine learning models on low-power microcontrollers. This talk will delve into the fascinating world of Tiny Machine Learning (TinyML) using the a Thumb-Size ESP32 CAM Dev Board: Seeed Studio XIAO ESP32S3 Sense. We will explore three significant projects demonstrating the wide range of possibilities with TinyML. Our journey begins with an Anomaly Detection and Motion Classification project. Here, we will use an Inertial Measurement Unit (IMU) sensor to identify unusual patterns and classify various types of motion. We will discuss collecting and preprocessing sensor data, training a machine-learning model, and deploying it onto the ESP32S3 for real-time inference. Next, we will explore Image Classification, showing how the XIAO ESP32S3 Sense, with its built-in camera, can identify and classify objects. We will discuss the challenges of working with image data, including handling high dimensionality and data variability, and demonstrate how we overcame these challenges to build a robust classifier. Finally, we will conclude the talk with a project on Keyword Spotting. Using the built-in microphone of the XIAO ESP32S3 Sense, we will demonstrate how to train a model to recognize specific spoken keywords. This portion of the talk introduces sound classification, a compelling field with many applications, from voice assistants to environmental sound classification. Throughout the talk, we will use the Arduino IDE and Edge Impulse Studio to create and deploy the TinyML models. Attendees will gain insights into data collection, pre-processing, model design, and impulse design.