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Find out the Best Resources to Learn Machine Learning
Machine learning is the study and use of algorithms and statistical techniques to make computers learn from data, without being explicitly programmed. These algorithms are mathematical models built on sample data, from which they learn patterns, to then make inferences about future or unseen data.
Machine learning algorithms can be grouped into 3 main categories, those suitable for supervised learning, those for unsupervised learning and those for reinforcement learning. In supervised learning, the computers use algorithms to learn from data where there is a “label” or a “target” available, or in other words, the outcome is known. Unsupervised learning groups a collection of techniques used to learn patterns from data where no “label” is available, that is, when we do not know the outcome. Reinforcement learning refers to the learning of actions that need to be taken in order to maximize a reward.
Some of the most common and widely used learning algorithms for supervised learning include Linear and Logistic Regression, Classification and Regression Trees, Random Forests, Gradient Boosted Trees, Support Vector Machines, Nearest Neighbors and Neural Networks. Among the algorithms for unsupervised learning we find clustering, like k-means and hierarchical clustering, One-Class Support Vector Machines, Isolation Forests, and…
