How to Learn NLP

Are you interested in developing the software and algorithms behind Alexa, Siri, Google Search, chatbots, Grammarly, and other technologies that involve human language? NLP, or natural language processing, is a growing field at the intersection of linguistics, machine learning, and artificial intelligence. 

Whether you are looking up your favorite restaurant or checking your spelling, you are reaping the benefits of NLP. The need for computers to understand human language will only continue to increase, especially as features such as voice recognition rise in popularity. 

In this master guide, we’ll show you how to get started in NLP.

What You Need to Know About NLP

NLP is the creation and application of techniques that enable computer systems to understand and respond to human (or natural) language. Computers cannot understand languages and contexts in the ways humans can (not yet, anyway); they need to transform the text and audio data they receive into formats they can analyze. 

There are many concepts under the umbrella of NLP. Here are a few of them:

• Text and speech processing: With NLP techniques, you can enable speech recognition, text-to-speech functionality, and the separation of chunks of continuous text through tokenization (where each smaller unit is a “token”).
• Morphological analysis: With NLP, you can use strategies such as lemmatization (removing the endings of words to return the base form of the word), morphological segmentation (dividing words into morphemes, or the smallest unit of language), and part-of-speech tagging (determining the grammatical part of speech of each word in a sentence). 
• Sentiment analysis: Sentiment analysis is the process of extracting subjective information from a corpus, or collection of texts. For example, a company can use sentiment analysis to conduct market research on Twitter (where tweets make up the corpus) or gauge Twitter users’ opinions about the company. If you work at a company that creates online products, you can even use Google’s Cloud Natural Language to understand customer opinions and make changes to a product’s design. 

Skills Needed to Learn NLP

To succeed in the field of NLP, you should have skills (or be willing to acquire skills) in the following areas:

• Machine learning algorithms. In NLP, much work is done with machine learning algorithms such as decision trees (hard if-then rules) and hidden Markov models (a way to model sequential data). 
• Neural networks. Artificial neural networks, modeled after neural networks in the human brain, are well-suited for dealing with large-scale, unlabeled data such as text. One neural network technique for NLP is using vectors to represent words. Words that have a similar context may have collinear vectors, or vectors that have the same direction. 
• Programming languages. Languages such as Python, R, and Java are commonly used in NLP. One of Python’s NLP libraries is the popular NLTK (Natural Language Toolkit), a library you can download to conduct part-of-speech tagging, lemmatization, stemming (reducing a word to its stem), chunk extraction (grouping words together), and more. R has a package for analyzing text called koRpus, and Apache OpenNLP is a Java tool for NLP.  

Why You Should Learn NLP

NLP is a growing field. As people continue to provide language input to computer systems, the number of datasets that can be analyzed and the resulting insights that can be leveraged will only increase. With NLP skills, you can: 

• Join companies that are building NLP applications. With your NLP knowledge, you could join teams at top companies that are doing NLP research and creating NLP products. For example, Google has a search engine that responds to user’s written and verbal queries, and Amazon’s Alexa is powered by NLP. Other companies like Grammarly and ProWritingAid use NLP to help people improve their writing. 
• Leverage insights for business purposes. If you work at a company, you can use NLP to learn about your customers’ preferences. Google builds tools for companies who want to conduct sentiment analysis and entity analysis (labeling entities within documents such as contact information and location). Amazon offers NLP services to companies who want to harness knowledge from their customer data.
• Take your interest in human language to the next level. NLP is fascinating! There is so much to learn with NLP, from statistics to determining the subjective meaning behind a word. If you have a linguistics background or simply want to combine language structures with computer science principles, NLP could be your data science niche. 

How Long Does it Take to Learn NLP?

If you want to transition into NLP professionally, you can learn the basic concepts in four to six months, depending on the skills you start with and the NLP focus area you choose. 

NLP is a broad topic that encompasses a variety of projects and specialties. There are people who get entire degrees in NLP and related subjects, such as machine learning and computational linguistics. 

Each programming language used in NLP (such as Python, R, and Java) has libraries and tools specifically for this field. You will need to learn these tools for your chosen language in addition to broader NLP concepts. 

Keep in mind that subjects such as statistics, machine learning, neural networks, and linear algebra are integral to NLP. At some point in your NLP journey, you will likely need to focus on these subjects individually, which can increase the time required to be ready for a job in the field. 

Learning NLP: A Study Guide

Resources such as books, courses, and tutorials can all help you learn NLP. Here are our top picks to get you started. 

NLTK Tutorial by Sentdex on YouTube

• Resource Type: YouTube Tutorial
• Price: Free
• Audience: Beginner

For data scientists interested in using Python for NLP, this tutorial series is an excellent starting point. You will learn how to install and use NLTK, a Python library, for NLP tasks such as tokenization, part-of-speech tagging, stemming, lemmatization, chunking, and much more. Additionally, you’ll be introduced to concepts such as working with corpora and conducting sentiment analysis.

You should be familiar enough with Python to write commands in IDLE, Python’s built-in text editor. That said, this video series does not assume any knowledge of machine learning or statistics, making it a perfect learning option for beginners. 

NLP Zero to Hero by TensorFlow on YouTube

• Resource Type: YouTube Tutorial
• Price: Free
• Audience: Beginner

With this series of videos, you will learn how to use TensorFlow, a software library for machine learning, for NLP applications. 

Designed for beginners in machine learning, these videos will teach you about tokenization, sequencing (turning sentences into sequences of tokens), and sentiment analysis. The videos use Python code examples to illustrate the lessons.

Stanford CS224N: NLP with Deep Learning on YouTube

• Resource Type: YouTube Lectures
• Price: Free
• Audience: Intermediate

These video recordings of classes taught by Christopher Manning at Stanford provide a valuable introduction to NLP. You’ll learn techniques and tools for performing NLP tasks, such as Google’s Word2vec (a set of strategies for turning words into vector representations which can then be used in NLP applications) and PyTorch (a machine learning framework). 

Not only will you get a handle on the details of NLP practices, but you’ll also gain a better understanding of human language and the challenges inherent in working with languages computationally. 

Data Science: Natural Language Processing (NLP) in Python by Udemy

• Resource Type: Course
• Price: $109.99
• Audience: Intermediate

With this course, you will learn how to build several NLP projects, such as a cipher decryption algorithm, a spam detector, and a model for stock market sentiment analysis. You will review basic machine learning concepts such as classification (categorizing input), regression (predicting the relationships between variables, such as the relationship between a house’s location and its price), and vectors. 

Other course concepts include NLTK and semantic analysis. You should have a basic knowledge of Python, machine learning, linear algebra, and probability before taking this course.  

Natural Language Processing in R for Beginners by Udemy

• Resource Type: Course
• Price: $49.99
• Audience: Beginner

If R is your NLP programming language of choice, check out this course. You will learn a variety of skills, including how to access text data with APIs (application programming interfaces), import data from Twitter and Wikipedia, measure emotion with sentiment analysis, and use part-of-speech tagging. 

You should have a basic understanding of R before taking this course. A certificate is awarded upon completion.  

Communities for People Studying NLP

Meetup Groups

As of this writing, there are over 1,200 groups on Meetup related to NLP, data science, deep learning, AI, and similar topics. 

Presenters often discuss how product designers and researchers are applying NLP in their daily work. For example, talks hosted by the San Francisco Bay Area NLP group address issues such as how developers at Grammarly built tone detection into their products and how Textio creates on-brand recruiting content for companies.

These groups are located all over the world, and many meetups are hosted online, giving you many opportunities to attend the events.  

LinkedIn Groups

LinkedIn has several groups related to NLP that you can join. These include Natural Language Processing People and Natural Language Processing Hackers & Tinkerers. If you are interested in NLP applications in languages other than English, you can also check out Arabic Natural Language Processing and Japanese Natural Language Processing. 

Twitter Accounts

There are some high-quality Twitter accounts where you can get links to articles, learn about NLP events, and generally stay informed about all things NLP. These include the Stanford NLP Group and MIT NLP Group, two of the top NLP labs in the world. Regional accounts also feature interesting blog posts and event details, such as New York-Natural Language Processing and Bay Area NLP. 

How Hard Is it to Learn NLP?

Learning NLP requires a lot of time and effort because the field has many components, techniques, and tools. It also encompasses topics such as machine learning, statistics, and linguistics, which can be challenging to master. 

There are labs and university programs that produce NLP practitioners, but there are also many people around the world who are learning on their own. By taking online courses and practicing NLP tools, you can join this community of NLP researchers, data scientists, and developers.

Will Learning NLP Help Me Find a Job?

As the amount of language data provided to computers grows, so does the need for developers and data scientists who can manage, analyze, and extract meaning from this data. 

Many positions for computational linguists, NLP developers, data scientists, or machine learning engineers require doctoral degrees. However, there are also job postings that focus on the mastery of certain skills, such as writing NLP algorithms and using programming languages and NLP tools, and less on the type of degree you have. 

Here are some quick facts about NLP in the job market:

• Salaries: According to Glassdoor, NLP engineers earn an average annual salary of $114,121. Computational linguists make an average of $91,821 annually. Indeed lists other roles where you can use your NLP skills, such as data scientist or machine learning engineer, at $122,964 and $151,255 respectively.
• Job Openings: Currently Glassdoor lists over 800 NLP engineer jobs with titles such as “Machine Learning Engineer, Natural Language Processing”, “IQ Bot Engineer/Architect”, and “Lead NLP AI Engineer”.
• Industry Growth: The Bureau of Labor Statistics does not have data on machine learning engineers or NLP engineers specifically. However, we can look at the numbers for computer and information research scientists, which as a position is comparable to the ones mentioned before. According to BLS, the number of openings for computer and information research scientists will grow by 15% from 2019-2029. This means that technologists with skills like research, machine learning, and data science will continue to be in high demand. 

Conclusion: Should You Learn NLP?

NLP, or natural language processing, is the field of computer science that deals with translating human (or natural) language into content that computers can understand. In return, computer systems like Alexa, Google Search, and Siri can give us verbal and text results.

NLP encompasses many tools and technologies, such as programming languages (Python, R, and Java); packages and toolkits (NLTK, Word2Vec, Google’s Cloud Natural Language); and complex sub-fields (neural networks, machine learning, statistics, and algorithms). 

If you choose to transition into a career as an NLP developer/engineer, computational linguist, or machine learning engineer, you’ll have the opportunity to explore a variety of technologies as part of NLP’s ever-changing landscape. You’ll also be in-demand and have access to high-paying job opportunities. 

If you’re excited about both the challenges and benefits of studying NLP, start your learning journey today!