Machine Learning Models: What They Are and How They're Made

Written by Coursera Staff • Updated on

Machine learning models are the backbone of innovations from finance to retail. Learn what machine learning models are and how they're made.

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Machine learning models are critical for everything from data science to marketing, finance, retail, and more. Today, only some industries are untouched by the machine learning revolution, which has changed how whole businesses operate and industries.

But what are machine learning models? And how are they made in the first place?

In this article, you will learn how machine learning models are created and find a list of popular algorithms that act as the base for many machine learning models. You'll also find suggested courses and articles to guide you toward machine learning mastery. 

Machine learning models vs machine learning algorithms

Though they are related, machine learning models and machine learning algorithms are not the same. 

Machine learning algorithms create machine learning models. The algorithms are trained using either labelled, unlabeled, or mixed data. Different machine learning algorithms achieve other goals, such as classification or prediction modelling, so data scientists use different algorithms as the basis for various models. As data is introduced to a specific algorithm, it is modified to manage a particular task better and becomes a machine learning model.

For example, a decision tree is a machine-learning algorithm for classification and prediction modelling. A data scientist looking to create a machine-learning model that identifies different animal species might train a decision tree algorithm with various animal images. Over time, the algorithm would become modified by the data and increasingly better at classifying animal images. In turn, this would eventually become a machine-learning model. 

Training a model for machine learning 

Machine learning models are created by training machine learning algorithms with either labelled or unlabelled data or a mix of both. As a result, three primary ways exist to train a machine learning algorithm: 

  • Supervised learning: Supervised learning occurs when a machine learning algorithm trains using "labelled data," or data tagged with a label so it can successfully learn from it. Training an algorithm with labelled data helps the eventual machine learning model classify data as the researcher desires.

  • Unsupervised learning: Unsupervised learning uses unlabelled data to train an algorithm. In this process, the algorithm finds data patterns and creates clusters. Unsupervised learning is helpful for researchers looking to find patterns in data that are currently unknown to them.

  • Semi-supervised learning: Semi-supervised learning uses labelled and unlabelled data to train an algorithm. In this process, the algorithm first trains with a small amount of labelled data before training with a much larger amount of unlabelled data. 

What are model parameters for machine learning?

Before a researcher trains a machine learning algorithm, they must first set the hyperparameters for the algorithm, which act as external guides that direct how the algorithm will learn. Hyperparameters include the number of branches on a decision tree, the learning rate, and the number of clusters in a clustering algorithm. 

As the algorithm gets trained and directed by the hyperparameters, parameters form in response to the training data. These parameters include the weights and biases the algorithm forms as it trains. The final parameters for a machine learning model are called the model parameters, which ideally fit a data set without going over or under. 

While a machine learning model's parameters can be identified, the hyperparameters used to create it cannot.

Types of machine learning models

Two types of problems dominate machine learning: classification and prediction. 

Machine learning approaches these problems using models derived from algorithms designed for either classification or regression (a method used for predictive modelling). Occasionally, the same algorithm can be used to create either classification or regression models, depending on how it gets trained. 

Below is a list of common algorithms for classification and regression models. 

Classification models in machine learning 

  • Logistic regression 

  • Naive Bayes 

  • Decision trees 

  • Random forest 

  • K-nearest neighbour (KNN)

  • Support vector machine

Regression models in machine learning

  • Linear regression

  • Ridge regression 

  • Decision trees

  • Random forest 

  • K-nearest neighbour (KNN)

  • Neural network regression

Learn more about machine learning. 

Whether you want to start a career as a data scientist or deepen your understanding of neural networks, a flexible online course can help you. Deeplearning.AI's Deep Learning Specialisation teaches you how to build and train neural network architecture and contribute to developing cutting-edge AI technology. Advanced qualifications are also available, such as Imperial College London's Master of Science in Machine Learning and Data Science.

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