Reflect for a minute on the tasks you perform in a single day--from the moment you wake up, to the time you fall asleep. If you were to list everything you did in this day, and included things like time, transactions, weather conditions, thoughts, where you went, what you ate...you would have a collection of raw facts about what you did in that one day. And that's exactly what data is!
Once these individual data points are collected and categorized (e.g. today’s hourly temperatures in Brooklyn, NY, USA), this data is now called information.
Information is valuable because past events may assist in the prediction of future events. For example, say you had a collection of all the hourly temperatures in Brooklyn, NY taken over a period of ten years. Assuming you are not using a weather app, if you wanted to find out what today’s high temperature for Brooklyn, NY would be, you can predict this temperature by taking the average of all those hourly temperature readings for today’s date over the past ten years. It may sound like a data analysis, but that's exactly what machine learning is!
Machine learning is one of the many methods of data analysis which uses a specific set of techniques or algorithms to make statistical generalizations about data (like the weather!) without explicit human intervention (McSweeney). This comes in handy when working with large datasets (aka BIG DATA), because these "algorithms give computers the ability to learn from data and then make predictions and decisions" (Crash Course) very quickly.
For more detail on applications of machine learning, check out our Happening page on this website.
The Process of Machine Learning