What is Machine Learning?

Setting up machines is the future!


Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: Supervised learning, Unsupervised learning, Semi-supervised learning and reinforcement learning. The type of algorithm a data scientist chooses to use depends on what type of data they want to predict.

  1. Supervised learning
     In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
  2. Unsupervised learning
    This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through datasets looking for any meaningful connection. Both the data algorithms train on and the predictions or recommendations they output are predetermined.
  3. Semi-supervised learning. 
    This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
  4. Reinforcement learning.
    Reinforcement learning is typically used to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.