Decoding the Differences: NLP vs Machine Learning
With AI tools dominating the world of brands to understand the habits, interests, and behavior of their audience, it’s vital to understand that not all AI tools are the same. As a business, you may have different visions and objectives than another business regardless of whether they’re in the same industry or niche as you. To ensure your vision is within hand’s reach, it’s integral to choose the AI tool that fulfills your objectives. For this purpose, you need to identify the contrast between natural language processing (NLP) and machine learning (ML). While both technologies are a branch of AI, their distinct functions - as discussed below - determine which would be a better companion for your business needs.
Machine learning adopts algorithms that have the ability to camouflage their existing skills by recognizing patterns from a set of data. By going through endless amounts of data, machine learning algorithms improve their proficiency and make independent decisions. This way brands can access both convenience and safety in their applications like user interfaces or security systems, such as image or facial recognition. In contrast, NLP is a part of AI whose core focus is modifying, analyzing, and integrating human language. The NLP methodology transforms words and phrases into logical terms, enabling computers to comprehend and interpret them.
When you go to buy a phone you compare one device’s features with another to purchase the best option in the market. Similarly, as a business, your productivity depends on the choices you make. Here’s a breakdown of how ML and NLP differ from each other so you can make the best investment for your business.
Machine learning algorithms look for patterns in a data set and then pass judgments based on those patterns that help businesses analyze key trends, answer consumer questions, or solve their problems.
NLP enables computers to understand and interpret human language. This enables machines to conclude, summarize, generate, and translate natural human text.
The primary focus of machine learning is accuracy and pattern recognition.
The primary focus of NLP is to train computers to conduct interactions in human/natural language.
ML applies the following two techniques:
1. Supervised learning: Here a model is trained on familiar input and output records to forecast future outputs.
2. Unsupervised learning: Here a model is trained to discover hidden patterns or core structures in data that is being inputted.
NLP applies the following two techniques:
1. Syntactic analysis: Involves training a model to analyze a set of symbols in computer language, natural language, or data models that follow proper grammar rules.
2. Semantic analysis: Trains a model to check whether a generated syntax tree accurately follows the rules of programming language.
Machine learning algorithms rely on vast data to precisely understand the relationship between input and output features.
NLP relies on machine learning for accuracy in providing responses and also to automate those responses
Machine learning algorithms require continuous monitoring and regular maintenance to keep them running
Since the NLP system is designed for individual and specific tasks, it fails to adapt to new structures and situations because of limited functions, making it less dependable.
Grammar checkers are AI tools using NLP to highlight and correct grammatical errors in a piece of text. While manual checking is still an option, grammar checkers increase the accuracy and speed of this task. Hence, if you’re running a blog or an account on social media, then a grammar checker can be of huge help to ensure that grammatical and language mistakes don’t leave a poor impression on your readers.
This is a practice of grouping information units like names, locations, organizations, or numeric expressions including date, money, time, etc from unstructured texts. This is beneficial for e-commerce businesses who can program their software and machines with NLP to find out what search terms are being used by users when they search for products online. Thus, shoppers can see customized searches every time they use the search engine for a specific query.
Real-Time Chatbots While chatbots were previously assigned with the task of fulfilling human requirements or requests, advancements in AI have now enabled them to process more complex requests. This has made the conversational experience of users more intuitive. Imagine a startup selling tracking software to other businesses. Chatbots can assist them with tasks like gathering customer data, scheduling meetings with experts, determining the next steps, and recommending solutions in a humane way.
Companies can feed their historical data into machine learning models to predict customer behavior. Imagine you’re a writer who wants to craft a blog post but is unsure whether to work on an AI-related or digital marketing topic. Through machine learning, you can access hundreds of keywords in no time. This is the best way to find ideas that your audience is most interested in and to understand how different blogs can be interconnected through internal links for an enhanced SEO score.
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