In supervised learning, we have several data points or samples, described using predictor variables or features and a target variable. In 2016, reinforcement learning was used to train Google DeepMind's AlphaGo, which was the first computer program to beat the world champion in Go.īut let's come back to supervised learning, which will be the focus of this course. Reinforcement learning draws inspiration from behavioral psychology and has applications in many fields, such as, economics, genetics, as well as game playing. Reinforcement agents are able to automatically figure out how to optimize their behavior given a system of rewards and punishments. There is also reinforcement learning, in which machines or software agents interact with an environment. This is known as clustering, one branch of unsupervised learning. For example, a business may wish to group its customers into distinct categories based on their purchasing behavior without knowing in advance what these categories maybe. Unsupervised learning, in essence, is the machine learning task of uncovering hidden patterns and structures from unlabeled data. When there are no labels present, we call it unsupervised learning. When there are labels present, we call it supervised learning. In the second example, there is no such label. Notice that, in the first example, we are trying to predict a particular class label, that is, spam or not spam. It could then assign any new Wikipedia article to one of the existing clusters. Another example: your computer can learn to cluster, say, Wikipedia entries, into different categories based on the words they contain. For example, your computer can learn to predict whether an email is spam or not spam given its content and sender. Machine learning is the science and art of giving computers the ability to learn to make decisions from data without being explicitly programmed. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data-all while using real world datasets. Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. If you're interested in learning this subject have a look at: Supervised Learning with scikit-learn ¶ This Jupyter notebook contains the Datacamp exercises and some of my personal notes.
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