Python Tutorial : Three flavors of Machine Learning

Want to learn more? Take the full course at https://learn.datacamp.com/courses/fundamentals-of-ai at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- To better understand Machine Learning, let's investigate its three most common flavors: Supervised, Unsupervised, and Reinforcement learning. Supervised learning is the most common flavor of machine learning in use today. Companies use it to predict employee performance, what product you're likely to buy next, are you likely to repay the loan you are applying for and much more. We use it to build models that predict categories or quantities based on some input measurements. So, if we are making a Fruit and Vegetable recognizer, the training inputs will be pictures and training outputs the labels stating which fruit or veggie is in the picture. The usage of output labels during training is where the name "supervised" comes from. There are two major problem types in supervised learning: Regression problems, when the output of interest is a quantity -- such as length, weight or oil prices; and Classification problems, where we want to predict categories, such as "metal or plastic", "positive or negative review". Most common models for tackling regression problems are Linear regression, Lasso and Ridge regression, as well as ARIMA models which are used for time-series forecasting. For classification, most common models are Logistic regression, Bayesian classifiers and Tree-based models (such as Decision Trees, Random Forests and Gradient Boosted Trees). As for neural networks, they are so versatile that, in the right configuration, they can be used to tackle both problems. Unsupervised learning owes its name to the fact that at training time it makes no use of the output labels -- it is only busy with capturing the relationships and patterns in process inputs. One typical problem we can solve in this way is finding groups of similar entities or events