Don't get bogged down in machine learning and natural language processing, that's the difference

 Don't get bogged down in machine learning and natural language processing, that's the difference

Machine Learning (ML) is a phrase that is being heard frequently in recent times due to AI tools. Because using machine learning models, AI writes like a human. With the rise of AI, Natural Language Processing (NLP) is another buzzword.

However, it is common to say that machine learning and natural language processing are synonymous. But although they are related to each other, there are definitely differences. Today in this article we will discuss those differences and how they work together in an AI tool:

What is machine learning?

One area of AI is machine learning. It is especially used in developing algorithms and mathematical models. Such models can develop themselves by analyzing data. Without the developer's instructions, those models can use data streams (transferring data at high speed) to learn patterns, make inferences, or make decisions.

Similarly, they enable machines to solve problems without any human assistance. Examples of machine learning are computer vision and defect detection systems used in self-driving vehicles.

What is Natural Language Processing?

This is an AI topic. It is used especially when analyzing and synthesizing 'text' and 'speech'. For this, NLP uses various methods to make computers understand human language. For this reason, NLP is used in places such as voice assistant, machine translation, text summarization, sentiment analysis, etc.

NLP has a great role in making AI and computer systems as user-friendly as they are today. Examples of this are Alexa, Siri, Google Assistant.

ML vs. NLP

So far we have come to know that Machine Learning (ML) and Natural Language Processing (NLP) are related topics to AI. Both help machines make decisions using models and algorithms. But the type of data they analyze is different.

Machine learning uses various methods to learn from data without programming the computer. Machine learning algorithms are used to analyze different types of data and learn from them.

On the other hand, natural language processing is considered a field within machine learning. It is a bit more focused on making computers understand human language.

NLP algorithm is used to analyze text only and learn from it. NLP, like ordinary chatbots, use general rules to work. But most NLP uses machine learning to understand language.

However, many methods such as deep learning, neural network, transformers, word embedding, decision trees, etc. come under machine learning.

Similarly, the Large Language Model (LLM) is a more advanced version of machine learning in non-natural language processing. A good example of this is ChatGPT 3. Models of natural language processing, machine learning.

Machine learning uses different types of natural language processing methods to understand text patterns. LLM is trained on large datasets of text and code. Therefore, it can do things like translating languages, preparing different types of writing such as poems/codes/songs/emails, and giving information.

Applicability of Machine Learning

We have already discussed the usefulness of machine learning above. But its usefulness is given in a little detail:

-Computer vision

- Image recognition

-Bioinformatics

-Medical diagnosis

-Product Recommendation

-Predictive analysis

- Market segmentation, clusterin and analysis etc.

 

Applications of Natural Language Processing

Comparatively, the utility of natural language processing is special and few. But many NLP uses machines to understand the language. When that happens, its field of application also increases. which are as follows:

-Text summarization

-Language translation

- Text Comparison

-Sentence Completion

-Smart Assistant

- Email filtering and spam detection

-Sentence analysis and text classification etc.

Overall, there are many similarities between machine learning and natural language processing. Both use algorithms to learn from data. The main difference between them is the type of data being processed.

Most of the current machine learning tools use generative models. Those tools can work without human input. So it may seem that these two are the same, but they are different.