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

Machine learning (ML) improves AI operations. Natural language processing (NLP) enhances understanding. While they are different terms, it’s not a matter of machine learning vs. NLP, but rather how they work together to improve customer experiences and increase employee productivity.

ML and NLP are both subsets of artificial intelligence (AI) and are frequently found together in AI systems and applications — but they are not interchangeable. Machine learning is a core component of AI development. Meanwhile, natural language processing leverages ML to improve semantic and syntactic recognition.

In practice, this means ML is found in every business AI application while NLP is applied to customer- or employee-facing applications that require contextual understanding. Consider the rise of AI agents — tools capable of working together with humans or autonomously to complete tasks. While NLP is an integral part of agent interactivity, the ability of agents to collect data, conduct analysis and make decisions all depends on machine learning.

Read on to learn more about machine learning vs. NLP.

What we'll cover:

• What is machine learning?
• What is natural language processing (NLP)?
• 5 differences between machine learning vs. NLP
• How machine learning and NLP work together
• How businesses use machine learning vs. NLP
• The future of machine learning and NLP

Machine learning (ML) is a subset of AI that trains systems to recognise patterns in data and take action based on those patterns. Over time, systems use algorithms to learn how to optimise tasks or streamline processes autonomously. The larger the dataset(s) available to ML frameworks — and the more time they have to analyse this data — the better the results.

Machine learning uses a three-step process to find patterns and improve outputs:

• Step 1: Algorithms are fed input data, then the machine analyses it for a potential pattern and makes a prediction.
• Step 2: The algorithm evaluates its prediction accuracy using known examples or existing classification rules.
• Step 3: Finally, machine learning carries out statistical analysis to determine where its predictive analysis can be improved to deliver more accurate outcomes.

Training ML for pattern recognition may be supervised or unsupervised. In a supervised approach, the algorithm is trained using known data inputs and outputs that have been labelled/classified by a human. This allows teams to verify data accuracy before expanding available datasets. Unsupervised learning uses an algorithm to analyse and cluster data that has not been labelled.

Machine learning helps AI tools make more human-like decisions based on available data and established rulesets. Common uses of ML include:

• Image recognition: ML tools are often used to identify and classify images. Consider an ML model learning to classify images into one of two categories: dog or cat. Using input image data and a basic descriptor of each animal, the algorithm classifies each image as a dog or a cat. It then evaluates the accuracy of these predictions against real-world data. Over time, the model's accuracy increases. This type of machine learning is often used with search engines — type in "dog," select “images” and you'll get a host of four-legged friends that aren't cats, horses or other animals.
• Recommendations: Machine learning algorithms also power recommendation systems. Familiar examples include suggestions for films, TV shows and other media based on what you've watched recently or products to buy based on your purchase history.
• Financial analysis: Financial analysis may also use ML to predict market trends or analyse the performance of specific mutual funds or stocks. By combining historical data with current patterns, algorithms can offer short- and long-term predictions.
• Cybersecurity: Detecting fraud, compromises or unauthorised system access are other use cases for machine learning. Tools compare baseline behavioural patterns with current data to pinpoint potential problems.
• Healthcare: In healthcare, ML may be used for records analysis, diagnostic prediction or treatment recommendations. For example, using public medical data and specific patient information, algorithms can help doctors create personalised treatment plans.
• Map routeing: Using real-time traffic data and existing map databases, ML tools can actively route and re-route drivers to minimise driving time and avoid accidents.
• Massive data sorting: Machine learning algorithms excel at large-scale data sorting. This is especially useful if companies have massive, disparate datasets that don't share any obvious characteristics.

Before we get into the differences of machine learning vs. NLP, we’ll dig a bit deeper into what NLP is.

Natural language processing allows AI systems to recognise, understand and communicate with human beings using human language. NLP uses large language models (LLMs) or small language models (SLMs) to accomplish this. This makes NLP useful for any user-facing AI process. Staff, customers or other end-users can speak or type questions in natural language, using conversational AI and receive a reply in the same format.

Natural language processing starts with data preparation. Several common techniques are:

• Text pre-processing: This process cleans up text to improve the chances of successful analysis.
• Stop word removal: Articles ("a," "an," "the") and prepositions ("in," "of," "on,") are removed to improve language processing.
• Tokenisation: This breaks sentences into individual words so a computer can understand the data.
• Lemmatising and stemming: Lemmatising reduces words to their root form so the model's input can be simplified and standardised. For example, words such as "dancing" or "danced" are narrowed to the root word "dance." Stemming is a quicker method of reducing words to their base.
• Part of speech tagging: Tagging diagrams sentences and categorises words in their naturally used formats as nouns, verbs and other parts of speech.
• Text classification: This process assigns classes or categories to text. For example, knowing whether text is from an online restaurant review or a news article helps NLP tools identify the correct sentiment.
• Disambiguation: The English language has many words that are spelt one way but have different meanings. Take the word "draw," which may describe the creation of a picture or the process of pulling something out. Disambiguation helps clarify context and meaning.
• Sentiment analysis: Classes and categories supplied by humans can help a computer understand whether online reviews are positive, negative or neutral.
• Speech-to-text: Computers can be trained on audio models to help them handle the task of handling speech-to-text.
• Translating text: This technique helps identify text language and enables translation into another language.
• Text generation: Having AI create copy involves algorithms and language models that help produce emails, web copy or marketing materials.
• Conversational agents: AI agents are different than traditional chatbots. Unlike those chatbots, which interact with humans based on a rules-based hierarchy, conversational AI agents can be trained using NLP to conduct human-like conversations.

Once data is prepared, NLP tools apply syntactic and semantic parsing processes to understand what's being asked and produce a reply. Syntactic parsing analyses the grammatical structure of sentences to identify nouns, verbs, adjectives and the relationships among these words. Semantic parsing focuses on the meaning or underlying sentiment of words.

NLP has been in the spotlight thanks to the rise of generative AI and agentic systems, such as Agentforce. Natural language processing (NLP) is also used in many other applications, such as:

• Speech and voice recognition: NLP supports voice recognition tools that can understand a human speaking and respond via voice or text.
• Speech and voice auto summarisation: NLP tools can also summarise the main points made during an audio conversation.
• Text analysis: Using NLP applications, businesses can analyse large pieces of text for common themes.
• Voice-activated assistants: Voice-activated assistants such as smart home speakers or mobile AI assistants use NLP to understand activation words.
• Chatbots: Underpinned by NLP, AI chatbots can answer simple user questions by accessing a connected database or escalate queries to human representatives.
• Predictive and generative text: Predictive text, such as the suggested words generated when you compose a text message or email, is powered by NLP.
• Sentiment analysis: Advanced NLP tools can analyse text or voice data for sentiment. For example, emails can be reviewed to determine whether customers are satisfied, frustrated or indifferent.
• Text and email filters: Natural language processing can also keep out unwanted messages. By applying filters that search for common words or phrases used in scams or phishing efforts, NLP tools can help limit spam emails or texts.

While machine learning and NLP are both subsets of AI, they're not the same. Five key differences in machine learning vs. NLP include:

1. Intent: The intent of ML is to find patterns and make predictions. The purpose of NLP is to improve language comprehension. Consider a database of user interactions. Where ML tools may seek out common denominators — customers typically do “X” after “Y” event — NLP tools might analyse these interactions to better understand how customers use language in service interactions.
2. Input: Machine learning inputs may be provided by model developers or the tools may be able to search multiple databases. Inputs for NLP tools, however, come directly from users. For example, teams may provide ML tools with sets of rules and questions to help improve understanding of a specific concept. While NLP tools are trained using basic conversational data, the bulk of their learning comes from unscripted conversations with users.
3. Learning: ML tools use supervised or unsupervised learning to analyse data. NLP solutions use syntactic and semantic analysis to parse user-provided content. In practice, this difference comes down to existing vs. new data. Machine learn​​ing algorithms draw on large amounts of existing data to discover connections, while NLP learning focuses on analysing the structure and sentiment of human input.
4. Autonomy: Machine learning tools can be given some autonomy to discover and analyse data. Natural language tools require more regular evaluations to ensure responses are accurate and reliable. Consider a machine learning application tasked with identifying birds. After being equipped with basic rules, the app digests massive datasets to discover new connections and improve outputs. While these outputs are periodically checked to ensure accuracy, the model is given a measure of freedom. In the case of NLP, its role in user-facing applications requires regular assessment to ensure outputs meet user expectations.
5. Data volume: ML algorithms are designed to handle massive data volumes from multiple sources. NLP applications depend on language data delivered in a specific format. Provide ML applications with access to multiple databases in different structural formats and they’ll find connections. NLP tools, however, improve when they’re exposed to conversational data that identifies speakers, responders, outcomes and ideal resolutions.

Machine learning provides the basis for NLP's understanding of human language while NLP provides a way to contextualise data inputs. Some common use cases include:

Machine learning can analyse unstructured data to identify text with specific characteristics and NLP can extract relevant insights. For example, a machine learning application could identify and collect recent customer service conversations while an NLP tool could analyse this data for common conversational themes.

NLP solutions can interact with users to identify workflow improvements and machine learning algorithms can identify how to implement those improvements via AI automation. Consider an employee looking to reduce the time spent searching databases for required information. Using an NLP interface, they could ask for recommendations to improve this process, which might include centralising data for easier analysis. Machine learning tools can then be given instructions to automate this data collection.

Machine learning excels at pattern identification and forecasting. NLP allows teams to ask direct questions of AI tools that underpin informed long- and short-term decision-making. For example, a CMO might ask an in-house NLP application to predict product demand for the coming quarter. This is accomplished using ML to analyse current and historical data for purchasing patterns.

NLP tools form the basis of customer-facing services such as chatbots or AI agents. Machine learning helps these tools improve over time to deliver more accurate outputs. Consider a chatbot that helps users find answers to common queries about product pricing, availability or delivery. Regular application of machine learning techniques can help the chatbot improve answer accuracy and find answers more quickly.

It's one thing to understand the generalised use of machine learning vs. NLP. It's another to apply these AI functions to business operations. Here are four specific use cases:

AI agents are powerful, customisable applications that help free up your sales staff for other tasks. For example, using Agentforce, companies can deploy AI-enabled sales development representatives to engage with customers 24/7.

Companies can use virtual agents like chatbots for faster, better customer service issue resolution. As noted by Salesforce research, agents can now resolve 83% of customer queries without a human being involved.

With advanced NLP, businesses can enhance human-to-human communication with tools that analyse text and offer spelling, grammar and context suggestions.

Generative AI tools continue to improve in their abilities to identify and parse human emotion. In combination with ML and NLP tools, this can help businesses build AI marketing campaigns that target specific customer needs or address unique pain points.

Machine learning is a commerce mainstay. By evaluating historical data, ML applications can suggest products and services that consumers might want or need. Applying NLP can further enhance commerce applications. For example, companies can have conversations (tailored to a customer’s preferences) directly on commerce sites or in the customer’s preferred messaging apps to help shoppers find products and make purchases.

The future of machine learning is bigger, faster and more accurate. As hardware evolves and algorithms become more sophisticated, expect machine learning to take on bigger datasets and deliver more accurate outputs. This, in turn, will enable improved AI decision-making by autonomous agents.

When it comes to NLP, improvements in machine learning will set the stage for enhanced syntactic and semantic recognition. Language applications will be able to better understand and respond to complex queries.

Ultimately, it’s not machine learning vs. NLP — it’s the combination and cooperation of these technologies that make improved AI outputs possible.

AI continues to evolve. Device makers are now adding neural processing units to devices. These microprocessors which mimic the processing function of the human brain, improving AI capabilities as machine learning and natural language processing models continue to make the best use of this hardware.

Put simply, AI has made the leap from hype to helpful as machine learning and natural language processing make it possible to create autonomous, accurate and articulate solutions that learn as they go and get better the more they learn. All with measurable ROI opportunities.

Ready to see how they can help your business? Learn more about Agentforce and how simple it is to build your own digital labour force.