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

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 whole businesses operate, but industries too.

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

In this article, you will learn how machine learning models differ from machine learning algorithms, how machine learning models are created, and find a list of popular algorithms that act as the base for many machine learning models. For those looking to learn even more, you will also find suggested courses and articles in the conclusion 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 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 will 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 machine learning algorithm that is 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. 

Training a model for machine learning 

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

  • Supervised learning: Supervised learning occurs when a machine learning 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

Supervised vs. Unsupervised Machine Learning

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. 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 should not be overfitted or underfitted to a data set. 

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

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 in machine learning 

  • Logistic regression 

  • Naive Bayes 

  • Decision trees 

  • Random forest 

  • K-nearest neighbor (KNN)

  • Support vector machine

Regression models in machine learning 

  • 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 start a career as a data scientist or simply deepen your understanding of neural networks, an online flexible machine learning course could help you. Deeplearning.AI’s Deep Learning Specialization teaches coursetakers how to build and train neural network architecture and prepare them to participate in the development of leading-edge AI technology. Advanced degrees are also available, like Imperial College London's Master of Science in Machine Learning and Data Science.

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