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Machine Learning (ML)

Reviewed by Dr. Tehseen ZiaCheckmark | Last updated: August 16, 2021

What Does Machine Learning (ML) Mean?

Machine learning (ML) is a sub-topic of artificial intelligence (AI) that focuses on building algorithmic models that can identify patterns and relationships in data. In this context, the word machine is a synonym for computer program and the word learning describes how ML algorithms will automatically become more accurate as they receive additional data.

The concept of machine learning is not new, but its practical application in business was not financially feasible until the advent of the internet and recent advances in big data analytics and cloud computing. That's because training an ML algorithm to find patterns in data requires extremely large data sets.

Today, machine learning plays an important role in a wide variety of business-related tasks that rely on predictive analytics -- including risk analysis and fraud detection, as well as voice and image recognition.

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Techopedia Explains Machine Learning (ML)

Machine learning projects are usually overseen by data scientists and machine learning engineers. The data scientist's job typically involves creating an hypothesis and writing code that will hopefully prove the hypothesis to be true.

What does a Machine Learning Engineer do?

An ML engineer's job focuses on machine learning operations (MLOps). Machine learning operations is an approach to managing the entire lifecycle of a machine learning model -- including its training, tuning, everyday use in a production environment and eventual retirement. This is why ML engineers need to have a working knowledge of data modeling, feature engineering and programming -- In addition to having a strong background in mathematics and statistics.

Ideally, data scientists and ML engineers in the same organization will collaborate when deciding which type of learning algorithm will work best to solve a particular business problem, but in some industries the ML engineer's job is limited to deciding what data should be used for training and how machine learning model outcomes will be validated.

What is a Machine Learning Model?

A machine learning model is simply the output of an ML algorithm that has been run on data. The steps involved in building a machine learning model include the following:

  • Gather training data.
  • Prepare data for training.
  • Decide which learning algorithm to use.
  • Train the learning algorithm.
  • Evaluate the learning algorithm’s outputs.
  • If necessary, adjust the variables (hyperparameters) that govern the training process in order to improve output.
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How is Machine Learning Trained?

In a typical ML setting, the algorithm requires a dataset comprises of examples where each example consists of an input and output. In such a setting, a typical objective of a ML algorithm is to update parameters of a predictive model to ensure that it predicts desired outcomes.

There are three main types of algorithms an ML engineer can use for training: supervised learning, unsupervised learning and reinforcement learning.

  • Supervised learning - the algorithm is given labeled training data (input) and shown the correct answer (output). This type of learning algorithm uses outcomes from historical data sets to predict output values for new, incoming data.
  • Unsupervised learning – the algorithm is given training data that is not labeled. Instead of being asked to predict the correct output, this type of learning algorithm uses the training data to detect patterns that can then be applied to other groups of data that exhibit similar behavior. In some situations, it may be necessary to use a small amount of labeled data with a larger amount of unlabeled data during training. This type of training is often referred to as semi-supervised machine learning.
  • Reinforcement learning – instead of being given training data, the algorithm is given a reward signal and looks for patterns in data that will give the reward. This type of learning algorithm’s input is often derived from the learning algorithm’s interaction with a physical or digital environment.

What Causes Bias in Machine Learning?

There is a growing desire by the general public for artificial intelligence – and machine learning algorithms in particular -- to be transparent and explainable, but algorithmic transparency for machine learning can be more complicated than just sharing which algorithm was used to make a particular prediction.

Many people who are new to ML are surprised to discover that it’s not the mathematical algorithms that are secret; in fact, most of the popular ML algorithms in use today are freely available. It's the training data that has proprietary value, not the algorithm used.

Unfortunately, because the data used to train a learning algorithm is selected by a human being, it can inadvertently introduce bias to the ML model that's being built. The iterative nature of learning algorithms can also make it difficult for ML engineers to go back and trace the logic behind a particular prediction.

When it is possible for a data scientist or ML engineer to explain how a specific prediction was made, an ML model may be referred to as explainable AI. When it is not possible to reveal how a specific prediction was made, either because the math becomes too complicated or the training data is proprietary, the ML model may be referred to as black box AI.

Machine Learning vs. Artificial Intelligence

The terms artificial intelligence and machine learning are sometimes used as synonyms because at this point in history, most AI initiatives are narrow. That's because the AI in use today typically relies on supervised machine learning to perform a single task.

In contrast, the goal of strong AI (also called artificial general intelligence) is to allow computers to successfully perform any intellectual task that a human being is capable of completing.

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Enterprise ApplicationsSoftwareComputer ScienceArtificial Intelligence Application Programming Interfaces (APIs)Analytics/BIMachine LearningData Science

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