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Machine Learning vs. NLP: What's the Difference?
Machine learning (ML) and natural language processing (NLP) are subsets of AI that work in tandem to improve data outputs.
Machine learning (ML) and natural language processing (NLP) are subsets of AI that work in tandem to improve data outputs.
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.
Machine learning (ML) is a subset of AI that trains systems to recognize patterns in data and take action based on those patterns. Over time, systems use algorithms to learn how to optimize tasks or streamline processes autonomously. The larger the data set(s) available to ML frameworks — and the more time they have to analyze this data — the better the results.
Machine learning uses a three-step process to find patterns and improve outputs:
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 labeled/classified by a human. This allows teams to verify data accuracy before expanding available datasets. Unsupervised learning uses an algorithm to analyze and cluster data that has not been labeled.
Machine learning helps AI tools make more human-like decisions based on available data and established rulesets. Common uses of ML include:
Before we get into the differences of machine learning vs. NLP, we’ll dig a bit deeper into what NLP is.
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Natural language processing allows AI systems to recognize, 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:
Once data is prepared, NLP tools apply syntactic and semantic parsing processes to understand what's being asked and produce a reply. Syntactic parsing analyzes 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:
While machine learning and NLP are both subsets of AI, they're not the same. Five key differences in machine learning vs. NLP include:
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Machine learning provides the basis for NLP's understanding of human language while NLP provides a way to contextualize data inputs. Some common use cases include:
Machine learning can analyze 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 analyze 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 centralizing 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 analyze 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 shipping. 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 generalized 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, customizable 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 analyze 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 data sets 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.
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