Computer Vision vs. Machine Learning: What’s the Difference?

Written by Coursera Staff • Updated on

Uncover the differences between computer vision versus machine learning. Learn about each topic in detail, explore typical applications, compare advantages and challenges, and discover where to learn more.

[Featured Image] A learner completes a lesson outlining computer vision vs. machine learning as part of their studies.

Machine learning involves training computer models to solve problems and make decisions, and computer vision refers to how machines interpret and understand the visual world. Both technologies involve developing machines that behave and act more like humans. The development of artificial intelligence (AI) has transformed the world in many ways, and these two cornerstone AI technologies may already be having a big impact on your personal and professional life in the form of facial recognition ID, social media, smart home devices, and digital voice assistants. Learn more about computer vision and machine learning and discover how they connect and differ.

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Analyse de données Google

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What is computer vision?

Computer vision seeks to provide computers with capabilities similar to human vision. It enables computer models to identify, classify, perceive, and interpret visual inputs and, with machine learning, eventually act on them (through decision-making, forecasting, problem-solving, etc.).

Applications of computer vision

Several processes in everyday life, such as language translation, self-driving cars, and damage identification, involve computer vision. You can look below at a more detailed review of the different applications of this AI technology:

  • Apps that scan text (from documents, magazines, menus, road signs, and more) and translate it into the user’s native language 

  • The ability of self-driving vehicles to identify and respond to other vehicles, lane dividers, road signs, traffic lights, pedestrians, bicyclists, and stray animals

  • Visual inspection software that uses computer vision to detect damages in machinery

Advantages of computer vision across industries

Computer vision has positive effects across many walks of life, with a few examples being soil monitoring, medical imaging, and task automation. Consider in more detail how it benefits various industries: 

  • Agriculture: If you’re a farmer, drones with computer vision can help you monitor soil and plant conditions for better crop maintenance and growth. 

  • Entertainment: Computer vision can detect how you respond to movies by tracking your eye movements and facial expressions.

  • Health care: Machines using computer vision may provide more accurate analysis of your X-rays or CT scans than human doctors.

  • Law enforcement: Computer-vision machines can help identify humans through fingerprint analysis or iris scanning. 

  • Manufacturing: With computer vision, assembly machines can perform monotonous tasks more accurately than humans, making manufacturing processes safer and more efficient. 

Computer vision challenges

Although new technologies offer many benefits, they also present some challenges, and computer vision is no exception. A few challenges of computer vision technology include:

  • The requirement of large data sets for training (and the corresponding time and expense involved)

  • Problems with accuracy in a system mimicking human vision, as various factors can affect human perception, including lighting, the sense of smell, and the sense of touch

  • Continual advancements in technology and corresponding changes in computer hardware and software

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What is machine learning?

As a specific branch of artificial intelligence (AI), machine learning (ML) uses algorithms and data to train computer models to learn and perform appropriate actions based on that learning. As a computer vision component, it enables computer models to respond appropriately when scanning, identifying, and classifying (or “seeing”) objects. When a computer model receives machine training, it relies on patterns and inference instead of human instructions to perform tasks. And, like you, the more machines learn, the smarter they get. 

Types of machine learning

Knowing about different types of machine learning, such as supervised and unsupervised, can help you further define the process. Common types of machine learning include:

  • Supervised learning: Use of a labeled data set (raw data pre-classified with labels) to determine performance accuracy in a computer model

  • Semi-supervised learning: Use of labeled and unlabeled data sets to train the computer model to label data sets independently

  • Unsupervised learning: Use of an unlabeled data set to train the computer model to find patterns and make predictions

  • Reinforcement learning: Use of a reward or punishment (i.e., a points system) for trial-and-error learning

Applications of machine learning

Machine learning helps improve daily and workplace processes by contributing to streaming recommendations, data analytics, and email automation. Take a look at some typical applications of ML, including:

  • Data analytics

  • Email automation

  • Natural language processing

  • Predictive analytics

  • Product recommendations

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

Advantages of machine learning across industries

To understand the many advantages of ML, you can explore its use across various industries. Consider these examples:

  • Banking and finance: Machine learning algorithms protect financial institutions and their customers and clients by analyzing massive amounts of data and detecting fraudulent transactions as they happen.

  • Business: Chatbots can supply immediate information to customers, making a good alternative to human call centers that put you on hold. It can boost customer satisfaction and save companies money.

  • Entertainment: Predictive ML algorithms help companies like Amazon analyze customer purchasing and preference data to recommend future purchases. By detecting patterns that humans might not find, computers can predict what you might enjoy.

  • Health care: ML-trained computers can detect medical issues that human doctors may miss. These include cancerous tumors, hairline bone fractures, and neurological problems.

  • Transportation: Ride-sharing apps provide quick and efficient ML service for rider-driver matching, price setting, traffic and road construction analysis, and route mapping.

Machine learning challenges

As with computer vision, machine learning also presents some potential challenges to users in terms of cost and accuracy. Below, you can review some common challenges of ML in more detail.

  • Cost: Machine learning requires expensive hardware and knowledgeable, high-paid staff.

  • Accuracy: Machine learning models trained on insufficient or inaccurate data can produce inaccurate output.

Difference between computer vision vs. machine learning

The key difference between machine learning and computer vision centers on the method. While computer vision trains computer models to identify and classify visual inputs, machine learning trains computer models to respond to various inputs based on prior learning experience. Although different, both technologies have a common goal: To train computers to perceive and process inputs and respond to them appropriately. 

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Getting started in AI on Coursera

Both computer vision and machine learning help make computers smarter, advancing the broader AI field. These AI technologies can also enhance your life at work and home and may provide exciting career possibilities.

You can further your AI knowledge by taking an online course on Coursera. Introduction to Artificial Intelligence (AI), offered by IBM, provides a good foundation for AI fundamentals, and the AI for Business Specialization, presented by the University of Pennsylvania (Penn), explores how incorporating AI can help improve business. These informative classes allow you to study and explore these topics at your own pace from your chosen place, as long as it has a reliable internet connection.

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Analyse de données Google

Mettez-vous sur la voie rapide d'une carrière dans l'analytique des données. Dans ce programme de certificat, vous apprendrez des compétences très demandées et bénéficierez d'une formation à l'IA dispensée par des experts de Google. Apprenez à votre rythme, sans diplôme ni expérience.

4.8

(151,455 ratings)

2,588,501 already enrolled

Beginner level

Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Analyse des Données, Création d'études de cas, Visualisation de Données, Nettoyage des données, Développer un portefeuille, Collecte de données, Feuille de calcul, Métadonnées, SQL, Éthique des données, Agrégation de données, Calculs des données, R Markdown, La programmation en R, Rstudio, Logiciel Tableau, Présentation, Intégrité des données, Détermination de la taille de l'échantillon, Prise de décision, Résolution de problèmes, Questionnement

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