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Introduction

Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, gradually improving its accuracy.

Machine learning is a critical component of the rapidly expanding field of data science. Algorithms remain trained to make classifications or predictions using statistical methods, revealing essential insights in data mining projects. These insights then influence decision-making within applications and businesses, ideally influencing key growth metrics. As big data expands and grows, so will the market demand for data scientists, who will be required to assist in the identification of the most relevant business questions and, ultimately, the data to answer them.

Machine Learning Networks

Artificial intelligence includes the subfields of machine learning, deep learning, and neural networks. On the other hand, deep learning is a subfield of machine learning, and neural networks are a subfield of deep learning. Because deep learning and machine learning remain often used interchangeably, it’s essential to understand their differences.

Deep learning and machine learning differ in how each algorithm learns. Deep understanding automates much of the feature extraction process, removing some of the manual human intervention and allowing for the use of larger data sets.

How Machine Learning Works

A Decision Process:

Machine learning algorithms remain typically used to make a prediction or classification. Your algorithm will estimate a pattern in the data based on input data, which can be labeled or unlabeled.

An Error Function:

An error function remains used to evaluate the model’s prediction. If there are known examples, an error function can compare them to determine the model’s accuracy.

A Model Optimization Method:

If the model fits the data points in the training set better, the weights are adjusted to reduce the difference between the known example and the model estimate. The algorithm will repeat this evaluation and optimize the process, automatically updating weights until an accuracy threshold remains reached.

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