“Machine Learning is a way of taking data and turning it into insights”. We use computer power to analyze examples from the past to build a model that can predict the result for new examples.
We encounter machine learning models every day. For example, when Netflix recommends a show to you, they used a model based on what you and other users have watched to predict what you would like. When Amazon chooses a price for an item, they use a model based on how similar items have sold in the past. When your credit card company calls you because of suspicious activity, they use a model based on your past activity to recognize anomalous behavior.
In Machine Learning, we talk about supervised and unsupervised learning. Supervised learning is when we have a known target based on past data (for example, predicting what price a house will sell for) and unsupervised learning is when there isn’t a known past answer (for example, determining the topics discussed in restaurant reviews).
Within supervised learning, there are classification and regression problems. Regression is predicting a numerical value (for example, predicting what price a house will sell for) and classification is predicting what class or type something belongs to (for example, predicting if a borrower will default on their loan).
Machine Learning can be used to create a chatbot, detect spam emails or image recognition.