This workshop offers a concise yet comprehensive introduction to multi-modal approaches in machine learning. We begin with an overview of what multi-modal learning entails, emphasizing how it combines data from various sources like text, images, and audio to improve learning accuracy. The session will cover key concepts such as data fusion and representation learning, essential for understanding how different data types can be effectively integrated. Attendees will explore real-world applications of multi-modal ML, including sentiment analysis and autonomous vehicles. The workshop also includes an overview of prominent multi-modal datasets and frameworks, equipping attendees with the knowledge to understand and evaluate multi-modal models. This session is tailored for individuals keen to broaden their understanding of machine learning by incorporating multi-modal approaches.