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Machine learning is the ability of a machine to improve its performance based on previous results.

Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications.

The key to machine learning is the algorithm — a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.

Machine learning algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data, or instance-based learning, where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. Instance-based learning does not create an abstraction from specific instances.

In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculations, data processing, automated reasoning and other tasks.

They use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. As the number of samples available for learning increases, machine learning algorithms adaptively improve their performance.

However, a guiding principle of machine learning science is that no one algorithm works best for every problem. This is because there are many factors involved, such as the size and structure of the dataset.

As a result, engineers commonly try different algorithms for a problem, while using a hold-out “test set” of data to evaluate performance and selecting the winner.

There are four general types of machine learning algorithms:

  1. Supervised — This category of algorithmconsists of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).
  2. Semi-supervised — Semi-supervised learning is similar to supervised learning, but instead uses both labelled and unlabeled data.
  3. Unsupervised — In this algorithm category, there is no target or outcome variable to predict / estimate.  It is used for clustering population in different groups.
  4. Reinforcement — The machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error.

Want to know more about machine learning? Tonex offers two machine learning courses:

Machine Learning Control Training, a 3-day course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.

Machine Learning Training Bootcamp, a 3-day course for data scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.

For more information, questions, comments, contact us.

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