Machine Learning Models: What They Are and How to Build Them

Written by Coursera • Updated on

Machine learning models are the backbone of innovations in everything from finance to retail. Read on to find out more.

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Machine learning models are critical for everything from data science to marketing, finance, retail, and even more. Today there are few industries untouched by the machine learning revolution that has changed not only how businesses operate, but entire industries too.

But what are machine learning models? And how are they built?

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

What is a machine learning model?

Machine learning models are computer programs that are used to recognize patterns in data or make predictions.

Machine learning models are created from machine learning algorithms, which are trained using either labeled, unlabeled, or mixed data. Different machine learning algorithms are suited to different goals, such as classification or prediction modeling, so data scientists use different algorithms as the basis for different models. As data is introduced to a specific algorithm, it is modified to better manage a specific task and becomes a machine learning model.

For example, a decision tree is a common algorithm used for both classification and prediction modeling. 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 become increasingly better at classifying animal images. In turn, this would eventually become a machine learning model. 

Read more: Decision Trees in Machine Learning: Two Types (+ Examples)

How to build a machine learning model

Machine learning models are created by training algorithms with either labeled or unlabeled data, or a mix of both. As a result, there are three primary ways to train and produce a machine learning algorithm: 

  • Supervised learning: Supervised learning occurs when an algorithm is trained using “labeled data”, or data that is tagged with a label so that an algorithm can successfully learn from it. Training an algorithm with labeled data helps the eventual machine learning model know how to classify data in the manner that the researcher desires.

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

  • Semi-supervised learning: Semi-supervised learning uses a mix of labeled and unlabeled data to train an algorithm. In this process, the algorithm is first trained with a small amount of labeled data before being trained with a much larger amount of unlabeled data. 

Read more: 7 Machine Learning Algorithms to Know

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Supervised vs. Unsupervised Machine Learning

What are parameters in 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. For instance, the number of branches on a decision tree, the learning rate, and the number of clusters in a clustering algorithm are all examples of hyperparameters. 

As the algorithm is trained and directed by the hyperparameters, parameters begin to form in response to the training data. These parameters include the weights and biases formed by the algorithm as it is being trained. 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.

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Parameters vs. Hyperparameters

Types of machine learning models

There are two types of problems that dominate machine learning: classification and prediction. 

These problems are approached using models derived from algorithms designed for either classification or regression (a method used for predictive modeling). Occasionally, the same algorithm can be used to create either classification or regression models, depending on how it is trained. 

Below you will find a list of common algorithms used to create classification and regression models. 

Classification models

  • Logistic regression 

  • Naive Bayes 

  • Decision trees 

  • Random forest 

  • K-nearest neighbor (KNN)

  • Support vector machine

Regression models 

  • Linear regression

  • Ridge regression 

  • Decision trees

  • Random forest 

  • K-nearest neighbor (KNN)

  • Neural network regression

Learn more about machine learning 

Whether you’re looking to become a data scientist or simply want to deepen your understanding of neural networks, enrolling in an online course can help you advance your career.

In Stanford and DeepLearning.AI's Machine Learning Specialization, you'll master fundamental AI concepts and develop practical machine learning skills in a beginner-friendly, three-course program by AI visionary Andrew Ng.

DeepLearning.AI’s Deep Learning Specialization, meanwhile, teaches you how to build and train neural network architecture and contribute to developing cutting-edge AI technology.

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Written by Coursera • Updated on

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