The expansion of artificial intelligence is not a new pattern. AI has existed for a more extended period than believed. Still, AI has lately become very famous due to tools like ChatGPT and Google Bard. Similarly, AI has turned into a robust instrument for automating outdated systems and processes in businesses. Due to this growth, there has been confusion between different terms such as natural language processing, machine learning, and deep learning. People commonly think that machine learning, NLP, deep learning, and AI are all the same terms. However, current discussions mainly focus on the natural language processing versus machine learning comparison. The first thing to comprehend is that NLP and ML are distinct sectors or branches of artificial intelligence. Here, we will delve into comparisons between natural language processing and machine learning.

Artificial intelligence is a broad term that envelops any technology that mimics human intelligence in machines. It aims to enable machines to think, learn, and resolve problems similarly to humans. AI applications range from search engines like Google, Tesla's self-driving cars, to recommendation systems on websites like YouTube and Netflix. It's crucial to know that ML and NLP are two vital divisions of AI that aid in creating AI solutions. Yet, NLP and ML are often used interchangeably with AI. It's essential to understand the detailed definitions of natural language processing and ML before comparing them. Also, investigating whether NLP and machine learning are interdependent is essential.

Machine learning stands as a renowned data analysis method that assists in automating processes for developing analytical models. Owing to its adoption by prominent names in the business and tech realm, machine learning has gained significant favor in recent times. For instance, machine learning is the force behind the successful Netflix recommendation system, known for delivering precise and relevant suggestions. By analyzing all customer interactions and touchpoints, machine learning can enhance personalization. It offers insights into what influences customer behavior, making it effective for forecasting. Additionally, machine learning helps solve problems like humans do, using automated processes and vast data. It also includes algorithms that contribute to tasks in areas like robotics, computer vision, and natural language processing.

Natural language processing, the other player in the NLP versus machine learning discourse, is another branch of AI. It empowers machines to grasp and interpret natural language, allowing them to understand text or voice data through various tasks. Some popular NLP tasks include automatic text summarization, speech recognition, and sentiment analysis. To comprehend NLP's significance, take an e-commerce platform where NLP transforms interactions with users, offering audio responses and voice modulation for a seamless experience. Furthermore, NLP ensures nuanced linguistic communication between users and the platform, aiding automatic translations and broadening the user base.

The query of whether NLP relies on ML holds true. The comparison between natural language processing and machine learning should only be viewed as a comparison between two AI subdomains. Machine learning serves as a vital tool for empowering NLP algorithms. Machine learning techniques like grammatical tagging and entity extraction aid in tagging parts of speech and extracting entities from text data. Additionally, lemmatization, stemming, and stop-word removal are employed in NLP utilizing machine learning to simplify data analysis and convert unstructured data to structured data. Both machine learning and NLP use statistical methods for recognizing entities, parts of speech, and sentiments.