What Is Machine Learning Classification?

Written by Coursera Staff • Updated on

Discover how machine learning classification works with AI programs to better understand how humans learn.

[Featured Image] A data scientist uses machine learning classification while working at her desk.

Machine learning classifications are different algorithms that help machines predict information to make successful business decisions, among other applications. A subfield of artificial intelligence (AI), machine learning helps systems learn independently instead of requiring a programmer to oversee pertinent data collection.

Unlike AI, machine learning has the ability to evolve, using one of several algorithms to train it how to learn to improve itself. Choosing the best algorithm in machine learning classification depends on what type of data you input and output, the amount of time and use of human labor going into the training and the reasons you are using machine learning.

This article explores the topic in greater detail to help you gain a deeper understanding of how machine learning classification works and the value it offers.

What is machine learning?

This subset of AI allows you to detect patterns in data and make predictions based on those patterns using algorithms. Machine learning essentially allows computers to do more tasks on their own. Before AI incorporated machine learning, programs such as rule-based classification were only used for their assigned tasks.

With machine learning, computers can utilize various techniques to process vast amounts of data through algorithms and select the required information. This helps the program learn from the provided data and improve itself. The program forms predictions based on specific examples by following patterns in the data and algorithms.

What is machine learning classification?

Machine learning classification is a method of machine learning used with fully trained models that you can use to predict labels on new data. This supervised machine learning method includes two types of learners that you can use to assign data to the correct category: lazy learners and eager learners.

Lazy learners memorize a training model instead of creating one from the beginning. When making a prediction, lazy learners review the entire data set and look at the closest neighbor. They are slower to make predictions because they wait for an entire data set to be complete.

Eager learner algorithms build a model from all available data before predicting prospective future datasets. By being proactive, eager learners have the preparation in place when sorting data. Eager learning algorithms require more training, but the outcome includes less time to make predictions with more accurate results.

The categories of algorithms for machine learning include supervised, unsupervised, and reinforcement learning. Machine learning classification falls under the category of supervised learning.

What is supervised learning?

Supervised learning aims to understand data in the correct context when answering a specific question. This method’s main benefit is that it trains algorithms by using labeled data sets. This ensures data accuracy when making predictions. As data is input into the model, the model adjusts the data to fit the algorithm and classifies it accordingly, defining the input and the predicted output.

In cases with unstructured data, supervised learning categories can recognize the data, categorize it, and make predictions. After the proper training, supervised data can not only compare data with proven data but also find any errors in the output and modify or correct them. Humans adjust the model until it can detect patterns independently and conclude the correct labeling when dealing with new data.

In supervised learning, a prediction task can be considered a classification task when the model focuses on the overall meaning of a text and the target variable is discrete.

What is machine learning classification used for?

In many industries, you can use machine learning classification to teach machines how and where to group a large amount of data. Once you determine the group of the data by the criteria, it is easier to understand and recognize the data patterns. Once patterns are in place, making more data-driven decisions with increasing precision is easier. Because different machine learning algorithms cluster information together into given categories, classification assigns large amounts of data into individually smaller values to make predictions easier, such as true and false or predefined output labels.

Since machine learning independently determines when new data is put in the system and the relationship between the input and output of data according to the classification, the system continually updates itself depending on the patterns it detects. In general, you can use machine learning to:

  • Finding and flagging errors or discrepancies in data entry or transactions

  • Using browser history to personalize shoppers’ preferences

  • Accessing and understanding market research patterns

  • Providing timely and accurate customer service on websites through chatbots

Examples of machine learning classification

Machine learning classification can be used in a variety of day-to-day applications. In the health care industry, researchers can use machine learning classification to predict new future diseases and whether someone might contract an infection. You could use machine learning classification to categorize documents and analyze learner feedback in education. The transportation industry can even use machine learning classification to predict traffic or road weather conditions.

How to start in machine learning classification

If you are looking to build a career in machine learning, which may include classification, you will likely want a strong foundation in math, science, statistics, data analysis, and programming. You will also need ample knowledge of machine learning tools, including software and techniques. Depending on the specific industry or field you want to pursue, experience or knowledge in that particular area is also essential.

In addition to these core skills, other skills that are important in machine learning and AI careers include the ability to communicate well with others, the capacity to think logically, and time management skills. A candidate must stay up-to-date on the latest technology because machine learning quickly evolves.

One career you could look into is machine learning engineering, in which you could earn an average annual salary of $126,293, according to February 2024 data [1]. Machine learning engineers have differing tasks but often design machine learning software, work with data scientists to create models, and develop analysis solutions for images and videos. 

Get started on Coursera.

Do you want to learn more about the different aspects of machine learning, including classification? Check out the University of Washington’s Machine Learning Specialization, a four-course series on Coursera. You’ll gain skills in working with data clustering algorithms and decision trees. Additionally, through a series of case studies, you’ll gain hands-on experience in significant aspects of machine learning, including classifications.

Article sources

  1. Glassdoor. “What does a machine learning engineer do?, https://www.glassdoor.com/Career/machine-learning-engineer-career_KO0,25.htm.” Accessed February 7, 2024.

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