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

Machine learning and artificial intelligence (AI) are widely used in the world today to analyze data, assess risk, manage inventory, and more. In fact, the AI market in India should grow at a CAGR between 25 per cent and 35 per cent by 2027 [1]. The market size for machine learning should expand to more than USD 17.8 billion by the end of 2030 [2].

Although people often use the terms artificial intelligence (AI) and machine learning (ML) interchangeably, they are actually distinct concepts that fall under the same umbrella. Understanding these distinctions can be helpful if you plan to pursue a career in the field. Read on to explore some benefits of each and find some suggested courses that will further familiarise you with the core concepts and methods used by both. 

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Explore machine learning and AI with us

Interested in a machine learning career? Consider taking Stanford and DeepLearning.AI's Machine Learning Specialization. Or what about AI? You can build job-ready skills with IBM's Applied AI Professional Certificate. Explore our collection of the best machine learning and AI courses.

What's the difference between machine learning and AI?

In simplest terms, AI is computer software that mimics the ways that humans think in order to perform complex tasks, such as analysing, reasoning, and learning. Machine learning, meanwhile, is a subset of AI that uses algorithms trained on data to produce models that can perform such complex tasks.

In many cases, machine learning performs AI tasks, so you may hear others use the two terms synonymously. However, AI actually refers to the general concept of creating human-like cognition using computer software, while ML has only one method of doing so. 

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What is artificial intelligence? 

Artificial intelligence (AI) is computer software that mimics human cognitive abilities to perform complex tasks historically done only by humans, such as decision-making, data analysis, and language translation. 

In other words, AI is code on computer systems explicitly programmed to perform tasks that require human reasoning. While automated machines and systems merely follow a set of instructions and dutifully perform them without change, AI-powered ones can learn from their interactions to improve their performance and efficiency. 

AI is an umbrella term covering a variety of interrelated but distinct subfields. Some of the most common fields you will encounter within the broader field of artificial intelligence include: 

• Machine learning (ML): Machine learning is a subset of AI in which programmers train algorithms on data sets to become machine learning models capable of performing specific tasks. 

• Deep learning: Deep learning is a subset of ML in which artificial neural networks (AANs) that mimic the human brain are used to perform more complex reasoning tasks without human intervention.  

• Natural Language Processing (NLP): A subset of computer science, AI, linguistics, and ML, natural language processing focuses on creating software capable of interpreting human communication. 

• Robotics: A subset of AI, computer science, and electrical engineering, robotics focuses on creating robots capable of learning and performing complex tasks in real-world environments. 

Watch this video from AI expert Andrew Ng and preview the AI for Everyone course:

What is machine learning? 

Machine learning (ML) is a subfield of artificial intelligence focused on training machine learning algorithms with data sets to produce machine learning models capable of performing complex tasks, such as sorting images, forecasting sales, or analysing big data. 

Today, machine learning is the primary way that most people interact with AI. Some common ways that you’ve likely encountered machine learning before include:

• Receiving video recommendations on an online video streaming platform. 

• Troubleshooting a problem online with a chatbot that directs you to appropriate resources based on your responses. 

• Using virtual assistants who respond to your requests to schedule meetings in your calendar, play a specific song, or call someone. 

AI vs. machine learning vs. deep learning 

AI, machine learning, and deep learning are sometimes used interchangeably, but they are each distinct terms.

Artificial Intelligence (AI) is an umbrella term for computer software that mimics human cognition in order to perform complex tasks and learn from them.  

Machine learning (ML) is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks. 

Deep learning is a subset of machine learning that uses several layers within neural networks to do some of the most complex ML tasks without any human intervention. 

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Real-world examples of AI

Chances are you’ve used an AI-powered device or service in your everyday life without even realising it. From banking programmes that check for shady transactions to automated spam filters that keep your inbox virus-free and video streaming platforms that recommend shows to you, AI and machine learning are increasingly woven into the fabric of our daily lives. Here are just a few of the ways that people use AI–and machine learning by extension–every day: 

Healthcare 

Healthcare produces a wealth of big data in the form of patient records, medical tests, and health-enabled devices like smartwatches. As a result, one prevalent way humans use artificial intelligence and machine learning is to improve outcomes within the health care industry. 

Some common applications of AI in health care include machine learning models capable of scanning x-rays for cancerous growths, programmes that can develop personalised treatment plans, and systems that efficiently allocate hospital resources.

Business

AI has had a significant impact on the world of business, where it has been used to cut costs through automation and to produce actionable insights by analysing big data sets. As a result, more and more companies are looking to use AI in their workflows. According to research by Goldman Sachs, businesses are investing huge amounts of money in AI, with global investments by businesses expected to reach USD 200 million by 2025 [1]. 

Supply chains 

Supply chains keep goods flowing all around the world. Yet, as supply chains become increasingly more complex and globally interconnected, so too does the number of potential hiccups, stalls, and breakdowns they face. To ensure speedy deliveries, supply chain managers and analysts are increasingly turning to AI-enhanced digital supply chains capable of tracking shipments, forecasting delays, and problem-solving on the fly.

AI capabilities

Artificial intelligence has a wide range of capabilities that open up a variety of impactful real-world applications. Some of the most common include pattern recognition, predictive modelling, automation, object recognition, and personalisation. In some cases, advanced AI can even power self-driving cars or play complex games like chess.

Consider starting your own machine-learning project to gain deeper insight into the field.

Benefits and the future of AI

AI and machine learning provide a wide variety of benefits to both businesses and consumers. While consumers can expect more personalised services, businesses can expect reduced costs and higher operational efficiency. 

It’s little surprise that the global market for AI will likely increase exponentially in the coming years. Grand View Research (GVR) projects the global market size for artificial intelligence to expand from USD196.6 billion in 2023 by 36.6 per cent between 2024 and 2030 [2]. Some common benefits for businesses using AI and machine learning in the real world include:

• The ability to quickly analyse large amounts of data to produce actionable insights.

• Increased return on investment (ROI) for associated services due to decreased labour costs.

• Improved customer satisfaction and experiences that business owners can tailor to meet individual customer needs.

Machine learning with Coursera

Machine learning and AI are increasingly integral to various sectors, driving innovations in data analysis, risk assessment, inventory management, and more. The distinctions between AI and machine learning are important for anyone pursuing a career in these fields, as AI encompasses broader human-like cognitive tasks while machine learning focuses on data-driven model training.

Whether you want to enter the field of AI professionally or just familiarise yourself with critical concepts to manoeuvre the modern world, you can start exploring the difference between machine learning and AI through online programmes on Coursera. DeepLearning.AI’s AI For Everyone course introduces beginners with no prior experience to central AI concepts, such as machine learning, neural networks, deep learning, and data science.

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AI For Everyone

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical ...

4.8

(46,586 ratings)

1,746,230 already enrolled

Beginner level

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Average time: 6 hour(s)

Learn at your own pace

Skills you'll build:

Deep Learning, Machine Learning