NLP vs. Machine Learning: What's the Difference?

The global artificial intelligence (AI) market is projected to reach a $244.22 billion valuation in 2025 and continue growing steadily in the coming years to reach $1.01 trillion by 2031 [1]. Within the AI space, natural language processing (NLP) and machine learning are two separate subfields, but NLP does utilize machine learning technology to teach computers to communicate using human language. Thus, although natural language processing and machine learning have their differences, some common ground exists between the two beyond their relation to AI, as machine learning plays an important role in NLP. 

Learn more about NLP vs. machine learning, including the use cases of these emerging technologies and potential career opportunities where you can put NLP and machine learning to use.

Do NLP and machine learning overlap?

Natural language processing and machine learning do, in fact, have some overlap. In addition to both falling under the larger umbrella of AI alongside other subfields such as deep learning, fuzzy logic, robotics, and computer vision, machine learning is also a primary component of NLP. This is because NLP relies on several elements, including machine learning, deep learning, and computational linguistics, to function. Specifically, machine learning directly contributes to NLP’s ability to learn to understand human language. Because human language contains aspects such as sarcasm, metaphors, differences in sentence structure, and intricate grammar rules, developers employ machine learning techniques to teach NLP applications these complexities.

What is NLP?

NLP is a type of artificial intelligence that gives computers the power to interpret and understand human language and generate it. By using various types of algorithms, NLP can learn to understand context and identify patterns in text data to perform tasks such as sentiment analysis, text classification, and text generation, giving you the ability to have conversations with computers, as seen in technologies such as virtual assistants, a few examples of these being Apple’s Siri or chatbots that provide customer service for businesses.

Natural language processing relies on machine learning, deep learning, and computational linguistics. Machine learning trains algorithms on vast amounts of text data, and the patterns the model identifies allow it to better understand text as it receives more data. Deep learning expands the ability of NLP models to contextualize text and speech through neural networks trained on unstructured data. Computational linguistics is important because it teaches NLP how to understand finer language details, such as grammar and structure.

What is NLP used for?

The overarching purpose of NLP is to help computers understand human language. By allowing computers to interpret text and speech and respond to it accurately, it’s possible to leverage the technology to automate several tasks and support efforts in various industries. Discover a few examples of NLP in action:

• Business analytics: By performing sentiment analysis, businesses can better understand how consumers feel about their products. NLP can scan social media posts for specific phrases to see what people say and use that insight to improve their offerings.

• Language translation: NLP provides more accurate, grammatically correct translations than its non-AI counterparts, which struggle with areas such as sentence structure when converting text from a language that doesn’t directly translate to another. 

• Chatbots: Businesses can automate their customer service by implementing NLP for chatbots and virtual assistants. Chatbots receive training to understand a wide range of potential user inputs and generate relevant responses.

• Health care: In the health care industry, NLP helps to automate documentation processes accurately and supports the analysis of unstructured medical data to recognize valuable insights that health care providers can use to improve patient outcomes.

Potential limitations of NLP

Although NLP has its benefits, it doesn’t come without some challenges, as it can be difficult to accurately teach these models to understand the nuances of human language. For example, sarcasm and the tone of your voice prove to be challenging for NLP to pick up on, leading to potential inaccuracies when evaluating semantics. It’s also possible to train NLP models on biased data, leading to less-than-ideal outputs in areas such as law enforcement or hiring, or have models simply struggle to interpret ambiguous words and phrases correctly.

What is the future potential of NLP?

NLP will only continue to advance as it improves the ability to contextually understand language and integrates with other forms of data outside of text, such as image or audio data. In the coming years, you can expect to see even more advanced virtual assistants and real-time language translation that helps open up communication around the world. 

What is machine learning?

Machine learning is a subset of AI that teaches computers how to learn like humans, enabling them to make accurate predictions, generate valuable insights that inform decision-making, and automate processes. The four types of machine learning models are supervised, unsupervised, semi-supervised, and reinforcement. Explore what differentiates each of these models and when it’s best to use each:

• Supervised learning: During supervised learning, you train machines using labeled data, providing the model with the correct output so it can learn to recognize patterns within the data and make accurate predictions when presented with new data. Supervised learning is ideal when the focus revolves around establishing the relationship between input and output data.

• Unsupervised learning: In unsupervised learning, the model undergoes training using unlabeled data, where the algorithm works independently to identify the relationship and patterns within the data. This makes it best suited for tasks such as exploratory data analysis, where the goal is to find relationships within raw data.

• Semi-supervised learning: Rather than solely relying on labeled or unlabeled data, semi-supervised learning uses both. This occurs over two separate phases, where you first train the model using labeled data before moving on to unlabeled data. Semi-supervised learning is suitable when you have access to significant training data, most of which is unlabeled.

• Reinforcement learning: Using a reward and punishment system, reinforcement learning provides the model with positive or negative feedback, so it will learn to work towards ideal outcomes. This machine learning method is ideal for complex environments prone to change and previously unseen circumstances, such as robotics.

What is machine learning used for?

The use of machine learning is now visible across many industries, including finance, health care, e-commerce, and cybersecurity, and this trend will only continue with an anticipated 32.41 percent compound annual growth rate (CAGR) from 2025 to 2031 [2]. Explore some of the ways machine learning is impacting different industries.

• Finance: Financial institutions use machine learning to assess whether they should give out loans, perform market analysis, and inform investment decisions.

• Health care: For health care providers, implementing machine learning presents opportunities to improve overall operational efficiency, discover lifesaving insights, and reach optimal outcomes. This technology can assist in accurately diagnosing diseases by having access to information such as health history, disease propensity, and lab results.

• E-commerce: Customers shopping online benefit from retailers offering personalized recommendations and customer service chatbots, which are made possible by machine learning.

• Cybersecurity: Machine learning models in cybersecurity are effective in spotting fraud, responding to cyber threats, and identifying cyberattacks to help prevent and manage them.

Potential limitations of machine learning

As with NLP, machine learning models depend greatly on the quality of data available for training, as low-quality and biased data will ultimately result in flawed outcomes. For organizations looking to implement machine learning, the expense of doing so is a potential limitation, as the upfront costs—resources and infrastructure—can be high. Beyond the cost of setting up your organization with machine learning models, you must also ensure you have staff with the expertise necessary to utilize the technology fully. 

What is the future of machine learning?

As machine learning becomes increasingly commonplace in the future, it will continue advancing by integrating with edge computing. Edge computing enables faster response time by reducing the distance data must travel for processing. When paired with machine learning, this means more efficient applications, such as devices that monitor your health in real time. 

NLP vs. machine learning: Key considerations

When you compare natural language processing and machine learning, their scope varies. NLP focuses on language-related applications, while machine learning is part of most AI business applications, giving it a broader reach. Machine learning also differentiates itself from NLP in its ability to function more autonomously. However, both NLP and machine learning are forms of AI, and NLP relies on machine learning to learn to interpret human language.

Preparing for an NLP or machine learning career

If you’re interested in pursuing a career in AI, you can earn a degree in a relevant field, such as a bachelor’s degree in computer science or data science, or pursue alternative education options, namely boot camps and online courses. You can find bootcamps and online courses that will allow you to develop relevant skills in applicable areas, including computer programming languages widely used in machine learning, such as Java, C++, R, and Python. Other in-demand technical AI skills to focus on developing include data processing and management; mathematical knowledge in areas such as calculus, linear algebra, probability, and statistics; and workplace skills such as communication, collaboration, and problem-solving.

Learn about NLP and machine learning on Coursera

NLP and machine learning are two distinct yet connected fields within artificial intelligence. NLP uses machine learning techniques to help computers understand human language, while machine learning also enables automation and predictive analysis. 

On Coursera, you can find highly rated courses to help grow your knowledge of NLP and machine learning. The Machine Learning Specialization from Stanford and DeepLearning.AI will allow you to build machine learning models, train neural networks, and apply machine learning to solve real-world problems.

You can also earn an IBM AI Engineering Professional Certificate, where you’ll have the chance to practice using supervised and unsupervised training methods and use NLP to develop transfer learning applications.