Machine learning, in its simplest form, uses past examples and pattern recognition to make predictions. You can then use the models to make predictions on future data. For example, machine learning could predict whether or not someone is more likely to buy a specific product or service. Looking at past behavior of previous customers as well as the target customer, you can to send a personalized promotional email to that customer that focuses on their buying criteria.
This is very important when it comes to data science because it allows a lot of the algorithms to be automated. If you can offload part of the predictive algorithms to a program then you can save a lot of time comparing and developing predictive analytics. This not only helps the data scientist to be more efficient but it allows the business to get the results they need quickly to adapt to coming changes and be more competitive in the market. This is the most important aspect to machine learning because it is the ultimate goal of the data scientist.
When it comes to machine learning, it is more important to have a lot of data then it is to build a better algorithm. I would imagine that since the idea of machine learning is to sift quickly through past data to make predictions about the future, that the algorithm doing the pattern analysis was the most important. I think Amazon described what Machine Learning is the best (even if it is in relation to their AWS Machine Learning offering).
“Machine learning (ML) is a technology that helps you use historical data to make informed business decisions. ML algorithms discover patterns in data and construct mathematical models using these patterns. Then, you can use the models to make predictions on future data. For example, one possible application of machine learning is detecting fraudulent transactions based on examples of both successful and failed past purchases.” – Amazon Web Services