Machine learning is an incredible subset of data analytics that enables computers to learn and make predictions without being explicitly programmed. It involves the development of algorithms and models that allow computers to perform tasks and make decisions based on data. Predictive analytics, on the other hand, utilizes machine learning algorithms to forecast future outcomes or trends.

Let's take an example to understand this better. Imagine you have a dataset of housing prices that includes features such as the number of bedrooms, square footage, and location. By applying a machine learning algorithm like linear regression to this dataset, you can build a predictive model that can estimate the price of a house based on its features.

Another popular machine learning algorithm is the decision tree. This algorithm breaks down a dataset into smaller subsets based on different conditions, creating a decision tree-like structure. Each level of the tree represents a decision or condition, which leads to different outcomes.

With the power of machine learning and predictive analytics, businesses can make informed decisions, identify patterns, and anticipate future trends with greater accuracy and efficiency.