What is Machine Learning?

Machine learning is a constant process, so accuracy improves when more data and feedback are added. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning (ML) is an algorithm. This allows a computer program to learn and adapt to new data without human intervention.

Machine Learning has a wide field of usage. For example, ML is often used by social networks that prepare a personal news feed for you. They track what type of content you like the most and suggest more content like that, avoiding posts that are not interesting for you.

How Does Machine Learning Work?

In a nutshell, there are four steps:

  • Select and prepare a training data set. Training data is a data set representative of the machine learning model’s data to solve the chosen problem.
  • Choose an algorithm to run on the training data set. The type of algorithm depending on the type and the amount of data in the training set. Generally, there are several types of common machine learning algorithms:

  • Regression algorithms. Linear regression is used to predict the value of a dependent variable based on the value of an independent variable. Logistic regression can be used when the dependent variable is binary in nature: A or B.
  • Decision trees. Decision trees use classified data to make recommendations based on a set of decision rules.
  • Instance-based algorithms. It uses classification to estimate how likely a data point is to be a member of one group or another based on its proximity to other data points.

  • Create a model based on a training data set. During training, you need to run data through the dataset and analyze the output. Doing this several times, adjusting weights and biases within the algorithm will eventually lead to building a model that returns to the correct time most of the time.
  • Continue improving the model. From this time, you can use the created mode and improve its accuracy over time.


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