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Iterative aspects of machine learning

machine

To find hidden insights without needing explicit programmes, machine learning uses algorithms which learn from previous data to help produce reliable and repeatable decisions.

It’s important to know the iterative aspect of machine learning, as models can have a mind of their own when exposed to new, fresh data.

Machine learning has “revolutionised” the world of testing, and now, its algorithms have the capability to apply complicated mathematical calculations quickly to large sets of data efficiently and quickly, on regular bases.

The essence of machine learning

According to the official 360Logica blog:

  • The essence of machine learning can be seen in Google’s self-driving car.
  • Machine learning application can be seen in everyday life like recommendation offers from Amazon, Netflix online.
  • It can be mixed with linguistic rule creation like knowing the customers what they are saying on social media like Twitter.
  • Machine learning can be used in fraud detection.

Following this, there appears to be a huge increase in interest towards machine learning, because of the advancements in data mining and analysis. Data storage is now more affordable; computation is powerful, as well as economical; and the volume and variety of data have increased.

Now, most activities on a day-to-day base are powered by machine learning – such as credit scoring, email spam filtering and web search results.

Widely used machines

According to 360Logica, the most widely used machines include:

  • Supervised learning algorithms are used where the desired output is known. The algorithm is provided with input sets and corresponding output sets. The algorithm analyses by comparing the actual output with the correct outputs.
  • Unsupervised learning is used against data with no historical labels. The algorithm should figure what is being shown without knowing the ‘Right Answer’.
  • Semi-supervised learning uses both labelled and unlabeled data with methods like classification, regression, and prediction. 
  • Reinforcement learning uses algorithms that discover through trial and error method. It is often used in gaming, navigation, and robotics.

Written by Leah Alger

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