what is machine learning

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What is machine  learning?


Machine learning algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.

Machine learning algorithms are used in applications such as computational finance (credit scoring and algorithmic trading), image processing and computer vision (face recognition, object detection, object recognition), computational biology (tumor detection, drug discovery, and DNA sequencing), energy production (price and load forecasting), natural language processing, speech and image recognition, and advertising and recommendation systems.

Machine learning is an integral part of data analytics, which deals with developing data-driven insights for better designs and decisions.

In many data analytics applications, machine learning models are deployed to the web or databases, or they are integrated into enterprise systems for on-demand analytics.


Build models to classify data into different categories. This can help you more accurately analyze and visualize your data.

You can use classification for applications such as credit scoring, tumor detection, and face recognition. Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks.


Build models to predict continuous data. With this information, you can make predictions about future data points. Regression applications include electricity load forecasting, and algorithmic trading.

Common algorithms for performing regression include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.


Find natural groupings and patterns in data. Clustering is used on unlabeled data to find natural groupings and patterns. Applications of clustering include pattern mining, medical imaging, and object recognition.

Common algorithms for performing clustering include k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, and subtractive clustering.


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