Some methods used in supervised learning include neural networks, naive bayes, linear regression, logistic regression, random forest, support vector machines (SVM), and more. Supervised learning helps organizations solve various real-world problems at scale, such as classifying spam in a separate folder from your inbox. A cross-validation process is then used to ensure that the model avoids overfitting or underfitting. The learning algorithm receives a set of inputs and the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is defined by its use of labeled datasets to train algorithms that classify data or accurately predict outcomes. The methods (see figure above) used to attain that accurate outcome fall into four major categories: The algorithm will repeat the evaluation and optimization process, updating weights autonomously until a certain level of accuracy has been met. If the model can fit better to the data points in the training set, then weights are adjusted to reduce the differences between the known example and the model estimate. If there are known examples, an error function can make a comparison to assess the accuracy of the model. Most are equipped with an error function that evaluates the prediction of the model. Machine learning utilizes a decision-making process that produces results based on the input data, which can be labeled or unlabeled. For example, if X number of families have geographically separate species with different color variants, there’s a good chance that several Y variants exist. They also can be nuanced, meaning that, although the platform may be the same, there can be subtle differences within the subset. These inferences can be obvious, such as “since the sky is blue today, it will most likely be blue tomorrow.” To that end, learning algorithms function on the basis that strategies, algorithms, and interpretations worked well in the past, so they’re likely to continue working well in the future. Data mining is another related field of study, focusing on exploratory data analysis through unsupervised learning. The study of mathematical optimization provides methods, theory, and application domains to ML. Some implementations of ML use data and neural networks in a way that mimics the working of a biological brain. ML is related to computational statistics, which focuses on making predictions using computers, but not all ML is statistical learning. This sub-field aims to create computer models that exhibit intelligent behaviors similar to humans, meaning they can recognize a visual scene, understand a text written in natural language, or perform an action in the real world. So much so that the terms are often used interchangeably and sometimes ambiguously as an all-encompassing form of AI. When companies employ artificial-intelligence programs, chances are they’re using machine learning. It’s also behind chatbots and predictive text, language-translation apps, and even the shows and movies recommended by Netflix. Such algorithms are used in myriad applications, including medicine, autonomous vehicles, speech recognition, and machine vision, where it’s difficult or unfeasible to utilize traditional algorithms to perform the required tasks. ML algorithms build a model based on sample data, or training data, to make predictions or decisions without being programmed to accomplish any given task. It’s a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Machine learning (ML) is a method of data analysis that automates analytical model building. Learn about types of supervised and unsupervised machine-learning approaches.

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