Understand machine learning
Machine learning involves feeding large amounts of data into an algorithm, which then processes this data to find patterns and relationships.
For example, if you provide an AI model with data about your past vacuuming habits and the layout of your home, the model that results could be used to program a robot vacuum to sweep your carpet in a similar pattern to you. Now imagine many of these robots were sold, transmitting all their vacuuming data back to the developer. Using machine learning to detect patterns could discover even more efficient movement patterns and optimize paths for all robot vacuums with the same design.
In the first video, you'll gain a better understanding of what machine learning is.
Machine learning is an AI technique that uses algorithms to create predictive models. These models are validated against known data, measured by specific metrics, and adjusted as needed. This process of learning and validation is called training. Through retraining, machine learning models improve overtime. There are also key methodologies on how to train models, for example, supervised learning, unsupervised learning, and reinforcement learning.
In the next video, you'll learn the difference between these methodologies, and be introduced to their combination in layers, called deep learning.