What is Machine Learning?

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What is Machine Learning?

Machine Learning (ML) is the study of algorithms and mathematical models that computer systems use to progressively improve their performance in a specific task. Machine learning algorithms construct a mathematical model of sample data, known as “training data”, to make predictions or decisions without being explicitly programmed to perform the task.

Machine learning is closely related to computer statistics, which focus on making predictions using computers. The study of mathematical optimization offers methods, theory and application domains for the field of machine learning.

Data mining is a field of study in machine learning and focuses on exploratory data analysis through unsupervised learning. In its application to business problems, machine learning is also known as predictive analytics.

How Machine Learning Works

Machine learning algorithms are generally classified as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide the desired input and output, as well as provide prediction accuracy information during algorithm training. Data scientists determine which variables, or characteristics, the model should analyse and use to develop predictions. When the training is complete, the algorithm will apply what has been learned to the new data.

Unsupervised algorithms need not be trained with the desired result data. Instead, they use an iterative approach called deep learning to review the data and come to conclusions. Unsupervised learning algorithms, also called neural networks, are used for more complex processing tasks than supervised learning systems, including image recognition, voice to text, and natural language generation. These neural networks work by combining millions of examples of training data and automatically identifying subtle correlations between many variables. Once trained, the algorithm can use its membership database to interpret new data. These algorithms were only feasible in the big data era because they require large amounts of training data.

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Examples of Machine Learning

Machine learning is being used in a wide range of applications today. One of the best-known examples is the Facebook news service. The news service uses machine learning to customize the content of each member. If a member stops moving frequently to read or receive posts from a particular friend, the news service will begin to show more of that friend’s activity on the channel earlier. Behind the scenes, the software simply uses statistical analysis and predictive analytics to identify patterns in user data and use those patterns to fill the news source. If the member no longer stops to read, comment on a friend’s posts, the new data will be included in the data set and the news source will be adjusted accordingly.

Machine learning is also coming in a variety of business applications. Customer relationship management (CRM) systems use learning models to analyze emails and have sales team members first respond to the most important messages. More advanced systems may even recommend potentially effective responses.

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Business intelligence (BI) and analytics providers use machine learning in their software to help users automatically identify potentially important data points. Human Resource Systems (HR) use learning models to identify the characteristics of effective employees and rely on this knowledge to find the best candidates for vacant positions.

Machine learning also plays an important role in driving cars on their own. Deep learning neural networks are used to identify objects and determine optimal actions to drive a vehicle safely along the way.

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Types of Machine Learning Algorithms

Just as there are almost unlimited uses of machine learning, there is no shortage of machine learning algorithms. They range from simple enough to highly complex. These are some of the most used models:

  • This kind of machine learning algorithm involves identifying a correlation, usually between two variables, and using that correlation to make predictions about future data points.
  • Decision trees. These models use observations on certain actions and identify an optimal route to reach the desired result.
  • K-means grouping. This model groups a specific number of data points into a specific number of groupings based on similar characteristics.
  • Neural networks. These deep learning models use large amounts of training data to identify correlations between many variables to learn how to process incoming data in the future.
  • Reinforced learning. This area of deep learning involves models that repeat several attempts to complete a process. The steps that produce favourable results are rewarded and the steps that produce unwanted results are penalized until the algorithm learns the optimal process.

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By | 2018-11-30T21:02:59+00:00 November 30th, 2018|Data Science|0 Comments

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