The machine learning vs NLP debate can get quite confusing. Both of them are advanced branches of Data Science and hence, are intertwined in many aspects. However, they’re not the same. If you wish to know how machine learning vs NLP differs from one another, keep reading till the end!
This article will help you understand the difference between machine learning and NLP as we’ll go point by point and highlight the distinctions and similarities between these two domains.
Table of Contents
Machine Learning vs NLP: Definition
To understand the difference between machine learning and NLP, we must first look at their definitions.
What is Machine Learning?
Machine learning is a method of data analysis that automates analytical model building. It is based on the idea that systems can learn from data, identify patterns and make decisions without requiring human intervention. It’s a branch of artificial intelligence and in the last couple of years, has evolved into one of the most in-demand sectors.
In simple terms, machine learning focuses on creating machines that learn automatically and don’t require human intervention. Some of the notable applications of machine learning are in:
- Self-driving cars
- Fraud detection
- Vision-based research
- Price prediction
- Natural language processing
Yes, you can use machine learning techniques in NLP and create models that solve the relevant problems automatically.
What is NLP (Natural Language Processing)?
Natural language processing is a combined field of Linguistics and artificial intelligence. It focuses on intelligent analysis of written language. Unlike us, computers need a lot of effort and systems in place for reading and analysing written text. They can’t simply go through the text and perform functions automatically as we do.
If you want a machine to perform specific tasks on written text (such as extracting information), you’ll need to use NLP. Even though it’s a niche field, NLP has numerous applications now. Some of the most popular applications of NLP include:
- Information retrieval
- Information extraction
- Sentiment analysis
NLP combines mathematics and data to engineer solutions that can understand and interpret natural expressions. Even your smartphone uses NLP to suggest spell checks or when it provides virtual assistance in the form of Google Assistant or Siri.
Machine Learning vs NLP: Salary
In terms of pay, both of these fields offer attractive packages. However, you should keep in mind that one of them is a complete domain while the other one is a subset of the same. Machine learning is a broader field and NLP falls under it. Therefore, there would be a significant difference in their career growth prospects.
Machine Learning Salary in India
The average pay of a machine learning engineer in India is INR 6.86 lakh per annum consisting of shared profits and bonuses. As a beginner, you can expect to earn around INR 3 lakh per annum in this field while the upper limit for the salary of a machine learning engineer goes up to INR 20 lakh per annum.
One of the biggest factors influencing your pay in this field is your expertise and experience. A machine learning engineer with one to four years of professional experience earns around INR 6.9 lakh per annum whereas a professional with five to nine years of experience earns INR 10 lakh per annum on average. Machine learning engineers with 10 to 19 years of experience make around INR 20 lakh per annum.
Apart from machine learning engineer, there are many other roles you can pursue in this field that offer lucrative salaries. Some additional roles you can pursue in machine learning are:
- Data scientist
- Data engineer
- Data analyst
- Software developer/engineer (AI/ML)
- ML Engineer
NLP Salary in India
As we mentioned earlier, NLP is a skill rather than a field. Unlike machine learning, where we can simply check the average salary of a specific role to determine the average pay of the industry, we can’t do the same here.
For NLP, we’ll get the average pay for the professionals that possess this skill. The median salary of professionals with the NLP skill in India is INR 9.77 lakh per annum.
Some prominent roles that require this skill include:
- NLP Scientist
- NLP Engineer
- Semantic Engineer
- Software engineer/developer (NLP)
Learning NLP skills will help you earn lucrative packages with plenty of opportunities to grow as an NLP professional. However, if you wish to grow in your career, you’ll need to focus on learning additional skills and stay up to date with the recent trends in your industry. Learn more about NLP salary in India.
Machine Learning vs NLP: How to Enter?
As NLP is a field that falls under machine learning, the difference between these two in terms of how to enter is negligible. Both are dependent on each other. If you want to become a machine learning professional, you’d have to learn about NLP.
Similarly, you can’t learn about natural language processing without first understanding the basics of machine learning. However, studying machine learning can be quite tricky. It has many advanced concepts and you must be adept at all of them to become a skilled machine learning professional.
Whether you want to become a machine learning professional or become an NLP expert, the best way to do so would be through a machine learning course. It will teach you the necessary concepts and skills you must possess to enter this field and become a professional.
Additionally, a course will give you a structured and step-by-step curriculum that helps you plan your studies and learn everything in proper order.
You can enrol in our PG Certification in Machine Learning and NLP program to master both of these domains. This course offers you:
- More than 250 hours of study material
- 5+ industry projects, assignments, and case studies
- 1:1 personalized mentorship from industry experts
The program lasts only for six months and is completely online. This means you can complete this program without leaving your job or disturbing your studies. You must have a bachelor’s degree with 50% or equivalent passing marks to join this program. Note that the course doesn’t require you to have coding experience.
Now that you’re familiar with the distinctions of machine learning and NLP, you can easily understand why they are so different. Machine learning focuses on creating models that learn automatically and function without needing human intervention. On the other hand, NLP enables machines to comprehend and interpret written text.
Which difference between machine learning and NLP intrigued you the most? Let us know by dropping a comment below.
What are the disadvantages of using NLP?
In the case of speech-to-text recognition, homonyms may create problems. If any word gets misspelled or misused, text analysis will become problematic. Extremely niche industries will require building or training their own NLP models. This is so because a model used in the health sector would be very different from that used in the educational sector. This is due to the difference in the language and terms used, so personalization of the model becomes a necessity. Thus, a lot of research and training is required if you want the NLP model to work efficiently, which in turn requires a lot of time.
Why is it necessary to have a knowledge of machine learning before knowing NLP?
In simple terms, NLP is trying to redefine how software comprehends the human language. NLP is used for a variety of tasks ranging from speech recognition to text analysis. It has a lot of applications in the industrial area. Machines can understand written or spoken language and execute tasks such as translation, keyword extraction, topic categorization, and more using natural language processing (NLP). However, machine learning will be required to automate these procedures and provide reliable results. Thus, no matter how well you train the NLP model, for its execution, machine learning will be required.
What is meant by tokenization?
Tokenization is a mandatory step in NLP that is used to break down a string of words into smaller units called tokens. This is done to make the words semantically useful. Its two types are word tokenization and sentence tokenization. Word tokenization breaks words inside a sentence, whereas sentence tokenization splits sentences within a text. Word tokens are usually separated by blank spaces, whereas sentence tokens are separated by stops.