Natural language processing (NLP) is a branch of artificial intelligence designed to process and analyze language-based data to improve communication between humans and computers. This technology allows computers to hear speech and read text and execute meaningful tasks accordingly. NLP is the driving force behind speech-recognition applications and online translation software.
Because of this, tech companies are always looking for professionals with NLP skills. Glassdoor estimates that natural language processing (NLP) engineers earn about $120,743 per year, which is more than the national average for all occupations.
If you want to start a career in this field, you should consider getting as much experience as possible by completing NLP projects that call out to you. Projects are a great way to build your portfolio and increase your marketability. This article covers some of the best NLP projects to help you sharpen your skills.
5 Skills That NLP Projects Can Help You Practice
Learning natural language processing is easier through projects. NLP projects are an efficient way of honing your skills as you learn through practice and repetition. Here are some key skills that NLP projects can help you improve.
- Mathematics. A solid foundation of mathematics and statistics can help you understand NLP. Familiarity with linear algebra, calculus, probability theory, and statistics is key in NLP, and you’ll be learning advanced concepts in these fields as you work on some of the projects below.
- Programming. NLP uses a wide variety of programming languages including Java, R, and Python. You need to master these programming languages to implement any NLP application. The best way to enhance your coding skills in these programming languages is through real-world projects.
- Data Structures. A solid foundation in data structures, especially trees, hash tables, and heaps is important in NLP development. NLP projects can help you practice your data structure skills, which will also come in handy for other machine learning projects.
- Algorithms. One way to develop your knowledge of algorithms is by practicing with NLP projects. NLP algorithms can be used to create meaningful summaries of books, news articles, blog posts, and other materials.
- Front End and User Interface Technology. UI design skills are useful for presenting visualizations of neural networks to people who might not have advanced knowledge. Taking advantage of front end frameworks like Flask and Django, you can build intuitive interfaces that make your NLP applications even better.
Best NLP Project Ideas for Beginners
You should consider easy and simple NLP projects when starting out. These projects will help you lay a solid foundation that will be useful for learning advanced concepts. Here are some of the best beginner NLP projects.
- NLP Skills Practiced: Python, decision trees, NLTK library, Bag-of-words model, Naive Bayes classifier
Chatbots are becoming increasingly popular on the Internet. They interact with customers to provide answers to questions in an efficient manner. In this project, you will hone your Python skills, particularly text classification, text preprocessing, tokenization, Parts-of-Speech tagging, and lemmatization, by building a complete chatbox application.
- NLP Skills Practiced: NLP algorithms
People prefer reading news stories and articles that are short. A summary writer app shortens long news articles using text summarization algorithms. This project will give you a better understanding of text summarization. You will gain insights into how NLP algorithms work when ranking sentences based on their significance or relevance.
- NLP Skills Practiced: Python, R, RESTful API
This project involves the use of NLP algorithms to identify and categorize opinions expressed in a given text. This is done to determine if the writer’s tone is positive, negative, or neutral. To work on this project, you’ll need to have a solid foundation in R or Python, as well as knowledge of the Naive Bayes classifier and RESTful API.
- NLP Skills Practiced: NLP algorithms, machine translation, fastText model
Predictive text generators are now very common. They are applications that use computational models to analyze the probability of two or more words appearing together in a text, thus helping people write messages and emails faster. In this project, you’ll put your skills to the test by developing a model that generates chains of words that are probably related.
- NLP Skills Practiced: fastText model
Search engines like Google use sophisticated neural network models to detect the language in which a webpage is written. As an NLP beginner, you can create your own language identifier by using the fastText model created by Facebook.
Here are some of the best intermediate NLP project ideas to consider to push yourself to the next level. Most of these projects will prepare you for advanced NLP projects.
- NLP Skills Practiced: Python, TextBlob
TextBlob is a Python library used to process text data. It provides a simple API for natural language processing tasks and can be used for classifying and analyzing feelings. In this project, the author of the post shows you how to use TextBlob and the Python programming language to write a program that corrects spelling errors.
- NLP Skills Practiced: Python, NLP libraries, textdistance
Almost all smartphone manufacturers incorporate autocorrect features into their smartphones. Autocorrect is a popular technology based on machine learning and, more specifically, on NLP. In this project, you will build an autocorrect application from scratch using Python and a library known as textdistance.
- NLP Skills Practiced: Python, NLP libraries
This project entails analyzing data from a WhatsApp chat group using several libraries, including NumPy, Seaborn, Plotly, and Pandas. First, you will extract the messages from the app and then format them using your Python skills so that you can analyze them. The author also teaches you how to create a word cloud to see the most commonly used words.
- NLP Skills Practiced: Python, NLP libraries
In this project, you’ll use basic data science libraries such as NumPy, Pandas, and Transformers to create a machine learning model capable of detecting fake news and misinformation. This project involves tokenization, vectorization, and modeling. You’ll be using a large, open-source CSV dataset.
- NLP Skills Practiced: Python, database management, NLP libraries
The US election is a key area of interest for machine learning engineers. It is possible to create a model that can predict the results of an election by performing sentiment analysis on social media apps like Twitter. In this project, you’ll work with Pandas, NumPy, and TextBlob to develop a model that performs sentiment analysis using datasets collected from Twitter.
Advanced NLP Project Ideas
Advanced NLP projects allow you to test your skills by tackling complex NLP problems. These projects are designed for expert or experienced neural network engineers and developers. Here are some of the best advanced NLP project ideas.
- NLP Skills Practiced: Python, NLP algorithms
This is an advanced project that will push your NLP skills to the limit. You will develop deep learning models that classify social media comments into different categories, the end goal being to eliminate hurtful or toxic comments from these platforms.
- NLP Skills Practiced: Database management, full stack development, NLP algorithms
Automatic speech recognition (ASR) involves using NLP algorithms to identify and analyze human voice commands. This is a very important technology with applications in many fields, including education, cyber security, and search engines. Building your own ASR model will take time and effort but it will help you develop in-demand skills and boost your employability.
- NLP Skills Practiced: Tokenization, database management, neural networks
This project uses neural machine translation, a recent variation of machine translation that mimics how the human brain functions. Building a neural machine translator from scratch will be a challenging project for even the most experienced engineers. You will need knowledge of preprocessing, word embeddings, and sequence to sequence encoding and decoding.
- NLP Skills Practiced: Jupyter Notebook, Keras, deep neural networks, deep learning
The goal of this project is to create a neural network model capable of detecting emotions from everyday conversations. You will build a model that can detect up to five different emotions from human language.
The applications of such a model are manifold, from adjusting the speed of cars to avoid collisions based on a person’s mood to helping marketers recommend products that match the consumer’s emotional state.
- NLP Skills Practiced: HTML, Python, deep learning
Paraphrase detection is a common NLP technique used to determine whether two sentences have the same meaning. In order to achieve a high level of accuracy, the sentences need to be syntactically and semantically analyzed. This is an academically challenging NLP problem that has been featured in the Kaggle Quora Question Pairs competition.
NLP Starter Project Templates
Starter templates help developers and programmers save time by giving them access to prewritten code and structure. These templates can be very helpful for beginners that need guidance to complete a project. Here are some NLP starter projects to consider.
- Tensorflow Project Template Architecture. This template is designed for any deep learning project, especially in NLP and TensorFlow. It is easy to use and will save you tons of time.
- Python Project Template. You can use this simple and intuitive template for starting multiple types of applications.
- PyTorch Natural Language Processing Project Template. This repository is great for computer vision tasks. It provides a low-level project structure to make the most of whichever framework you choose to employ.
Next Steps: Start Organizing Your NLP Portfolio
The best way to showcase your skills in the tech industry is by highlighting your best creations in a portfolio. Developers and programmers use portfolios to show employers what they are capable of. Here are a few tips for organizing your portfolio.
Focus on Readability and Clarity
Portfolios should be easy to read and navigate. Whether you are looking for a job in front end or backend development, your portfolio should be concise and clear. Be sure to include project descriptions that explain the project, your process, and the issues you encountered.
"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"
Venus, Software Engineer at Rockbot
Show Your Full Range of Skills
Include projects that touch on different skills and topics to give employers an idea of the extent of your knowledge. Having variety in your portfolio shows that your expertise spans a wide spectrum of topics.
Include Links to GitHub Repositories
GitHub is a popular platform that lets you work from anywhere and collaborate with other developers. Creating a GitHub repository for your projects and including the links in your portfolio will make your work accessible to any employer interested in hiring you.
NLP Projects FAQ
How do I start an NLP project?
An NLP project consists of several phases. Oftentimes, you’ll start with data collection and reprocessing. This is followed by model development, feature extraction, assessing language models, and deployment.
Are NLP projects helpful?
Yes, NLP projects are very helpful if you are looking to learn about machine learning and artificial intelligence. Through practice and repetition, you will develop problem-solving and technical skills.
How is NLP used?
Common applications of natural language processing include chatbots and virtual assistants. Other real-world uses of NLP are automatic text summarization, sentiment analysis, text labeling, and neural machine translation.
What are some good final-year projects related to natural language processing?
Some popular NLP projects include creating a chatbot and a neural machine translator, predicting the results of elections based on sentiment analysis on social media, and building a speech recognition system.
Rate this Article
- Step 1: Programming. I was really hesitant whether to put programming as the first step of the second one. ...
- Step 2: Math, Statistics, and Probability. ...
- Step 3: Text Preprocessing. ...
- Step 4: Machine Learning Basics. ...
- Step 5: NLP Core Techniques. ...
- Step 6: Future Natural Language Processing.
Unlike other language models, BERT has only been pre-trained on 2,500 million words of Wikipedia and 800 million words of Book Corpus and has been successfully used to pre-train a deep neural network. According to researchers, BERT has achieved 93.2% accuracy, which surpasses previous results of accuracy.How many techniques are there in NLP? ›
Natural Language Processing (NLP): 7 Key Techniques.How do you create an online portfolio for a data science beginner? ›
Be active on the platform by using new datasets and creating data analysis or machine learning models. Participate in discussion, learn from experts, and ask for help. Use web scraping to publish a new dataset. Participate in most competitions to learn several types of machine learning problems and to earn badges.Which NLP course is best? ›
- Coursera's Natural Language Processing Specialization. ...
- Udemy's NLP - Natural Language Processing with Python. ...
- Stanford's Natural Language Processing with Deep Learning. ...
- Udacity's Master Natural Language Processing. ...
- SpaCy's Advanced NLP.
- NLP with Deep learning By Stanford University. ...
- NLP course from Coursera. ...
- NLP Course By Carnegie Mellon University. ...
- NLP course by Oxford University (GitHub Repository) ...
- Natural Language Processing (NLP) for Beginners Using NLTK by Udemy.
- 4 Things To Do Before You Start Studying NLP. How to get started the right way! ...
- Learn Python basics and libraries. print("Hello world!") ...
- Get your maths straight. Photo by Antoine Dautry on Unsplash. ...
- Set up a working environment. Photo by Emile Perron on Unsplash. ...
- Start reading scientific papers.
On average, successful students take 3 months to complete this program.Is NLP difficult to learn? ›
Natural Language processing is considered a difficult problem in computer science. It's the nature of the human language that makes NLP difficult. The rules that dictate the passing of information using natural languages are not easy for computers to understand.Is XLNet better than BERT? ›
XLNet has a similar architecture to BERT. However, the major difference comes in it's approach to pre-training. BERT is an Autoencoding (AE) based model, while XLNet is an Auto-Regressive (AR). This difference materializes in the MLM task, where randomly masked language tokens are to be predicted by the model.
But the differentiator that truly sets T5 apart from BERT-style models is that it does not output a label or a span of the input to the input sentence, but the output is a text string as well.How can you increase the accuracy of an NLP model? ›
- Predict fewer than 1,000 characters as output : If it is text generation model, try to generate less than 1000 characters. ...
- Remove stopwords with proper thought : Removing stop words in text processing is one of the important steps.
“Doing NLP on yourself is like playing tennis alone. You can do it, but it's very slow.” The Problem Is That You Can't Be In Two Places At Once. You can't be in your head, having the feelings that create the state you want to work with, and at the same time be outside of yourself, analyzing what might be going on.What is wrong with NLP? ›
There is no scientific evidence supporting the claims made by NLP advocates, and it has been discredited as a pseudoscience. Scientific reviews state that NLP is based on outdated metaphors of how the brain works that are inconsistent with current neurological theory and contain numerous factual errors.What are the 5 NLP Insights successful meetings? ›
- NLP Technique 1: Stepping into Someone Else's Shoes. ...
- NLP Technique 2: Determining the Outcome of the Meeting. ...
- NLP Technique 3: Creating Movies in Your Mind (Association and Dissociation) ...
- NLP Technique 4: Using a Model of Excellence.
In general, your portfolio should showcase your best or latest work. Try to include projects that highlight your ability to: Scrape data from websites: Show your code, and use hashed comments to explain your thinking.Who is the best NLP coach in the world? ›
Tim Hallbom. Tim Hallbom is widely considered to be one of the most talented and accomplished NLP trainers in the world.Are NLP courses worth it? ›
Yes – if you're curious about exploring communication and influence, and genuinely want to improve your life, and if you are prepared to put in the work to do so. NLP is particularly effective if you want to move forward to the next stage of your life journey.Can I learn NLP online? ›
Since most institutes are now offering their best NLP courses online, you can learn and upskill from the comfort of your home. If you are looking for a short term Machine Learning Course check out IIT Delhi's Machine Learning Program in association with upGrad.What are the 7 key steps for getting started with natural language processing NLP project? ›
- NLTK (Natural Language Toolkit)
- BS4 (Beautiful Soup 4)
- Step 1 — Import Libraries.
- Step 2— Reading the Page.
- Step 3— Data Cleaning.
- Step 4— Tokenization.
- Step 5— Data Visualization.
No. Deep learning algorithms do not use NLP in any way. NLP stands for natural language processing and refers to the ability of computers to process text and analyze human language.Do I need to learn deep learning for NLP? ›
Natural language processing is not “solved“, but deep learning is required to get you to the state-of-the-art on many challenging problems in the field.How can I learn NLP for free? ›
- 10 Free Resources for Learning Natural Language Processing. ...
- Introduction to Natural Language Processing. ...
- Study guide from the University of London. ...
- Awesome NLP. ...
- Stanford lectures on natural language processing with deep learning. ...
- NLP with pytorch. ...
- Speech and language processing.
A Bachelor's degree in Computer Science, Mathematics, Science, Physics, or a related field is required to work as an NLP Engineer. Depending on the nature of the function inside a corporation, different admission requirements apply.Who is Jay Alammar? ›
Speaker: Jay Alammar
Jay is a Partner at STV, a $500m tech venture capital fund.
The time it takes to learn NLP depends on the background you have in mathematics. If you are a computer science graduate, it will take you about a month. And, if you are a beginner who has no idea about NLP, it'll take you 3-4 months to learn NLP from scratch.Is NLP a good skill? ›
NLP was one of the hottest skills in 2019 and 2020 for good reason. Companies have a lot of text to... NLP was one of the hottest skills in 2019 and 2020 for good reason. Companies have a lot of text to work with and many applicants to apply it across the business.Is NLP the future? ›
According to the research firm, MarketsandMarkets, the NLP market would grow at a CAGR of 20.3% (from 11.6 billion in 2020 to USD 35.1 billion by 2026). Research firm Statistica is even more optimistic. According to their October 2021 article, NLP would catapult 14-fold between the years 2017 and 2025.Who works in NLP? ›
The primary job of NLP Scientists is to teach machines how to understand the nuances of human languages. Hence, they must be fluent in the syntax, spelling, and grammar of at least one language (the more, the better). Also, they should have basic data science and machine learning (ML) skills.Is NLP machine learning or AI? ›
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand the human language. Its goal is to build systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification.
Both Computer Vision and NLP (natural language processing) have been good at tackling certain circumscribed tasks. Still, they are both progressing at a rather slow speed and the NLP field is even lesser than computer vision.Is NLP solved? ›
The NLP domain reports great advances to the extent that a number of problems, such as part-of-speech tagging, are considered to be fully solved. At the same time, such tasks as text summarization or machine dialog systems are notoriously hard to crack and remain open for the past decades.Is RoBERTa better than BERT? ›
2. RoBERTa stands for “Robustly Optimized BERT pre-training Approach”. In many ways this is a better version of the BERT model.Is Electra better than BERT? ›
The ELECTRA paper indicates that the electra-small model significantly outperforms a similar-sized BERT model. Distilled versions of Transformer models sacrifice a few accuracy points for the sake of quicker training and inference.Is DistilBERT faster than BERT? ›
DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark.Is there anything better than BERT? ›
XLnet outperforms BERT on 20 tasks, often by a large margin. The new model achieves state-of-the-art performance on 18 NLP tasks including question answering, natural language inference, sentiment analysis, and document ranking.What is DistilBERT? ›
DistilBERT is a small, fast, cheap and light Transformer model based on the BERT architecture. Knowledge distillation is performed during the pre-training phase to reduce the size of a BERT model by 40%.How was GPT-2 trained? ›
Both are unsupervised transformer models trained to generate text by predicting the next word in a sequence of tokens. The GPT-2 model has 1.5 billion parameters, and was trained on a dataset of 8 million web pages.