Kickstart Your NLP Career with This Easy-to-Follow Roadmap
Learn NLP and build your own ChatGPT
ChatGPT is like a Swiss Army knife that can write poetry or articles, interpret images, code a website, build a game, and find bugs in code Moreover, you can upload datasets, generate reports, and download them in seconds using ChatGPT’s Code Interpreter.
It is actually a conversational robot that built with LLMs (Large Language Models). LLMs are very popular right now. Trust me, LLMs are state-of-art technology that will shape the future. Using these models, you can build your own AI tools like ChatGPT.
Today, I’ll present you a roadmap to learn NLP. Here are topics we’ll cover in this article:
- First, we’ll take a brief look at what NLP is.
- Then, we’ll discuss the skills you need to master this domain.
- Next, we’ll explore frameworks and libraries to help you easily implement your projects.
- Lastly, we’ll go through various learning resources to be master in NLP.
Let’s dive in!
What is NLP?
As you know, there are two types of languages. The first one is a machine language like Python or Java and the second one is a human language like English or Spanish. We leverage the term “natural language” to distinguish human language from machine language.
Natural language processing (NLP) is a subfield of AI that aims to extract insights from natural language. It has many real-world applications such as language translation, sentiment analysis, text summarization, speech recognition, chatbots, etc.
You can use both classical and modern approaches in NLP projects. As a rule of thumb, if your data is small, you can leverage machine learning algorithms such as logistic regression and support vector machines. On the other hand, if your data is big, you can utilize deep learning techniques based on Transformers.
Ok, we took a quick look at what NLP is. Now, let’s take a look at the skills you need to succeed in your NLP projects.
The Skills You Need to Master NLP
NLP is an amazing field. You can build awesome apps in NLP. But you need to acquire some skills. In this section, we’re going to discuss these skills.
1. Programming Language
To implement NLP projects, you first need to learn a programming language. There are many programming languages you can use in NLP. But, Python is the most widely used programming language in this field.
Many projects are built using Python due to its ease of use. In addition, you can use many great libraries with Python. We’ll talk about these libraries below.
These libraries were built in C++ which is close to machine language. Python is an interpreted language, which means it can be slower than compiled languages like C++. But, you can use these libraries with Python.
You can also employ C++ and Java for your NLP projects. However, Python is king in NLP.
Here are some resources you can use to learn Python:
- Freecodecamp’s learn Python — full course for beginners
- Mosh’s Python tutorial — Python full course for beginners
- Codecademy’s Python course
- Harvard CS50’s introduction to programming with Python
2. Mathematics
I know many people don’t like math. But, this skill is one of the key areas you need to know for NLP projects.
Don’t worry, you don’t need advanced math skills for NLP. Believe me, it is sufficient to have a basic understanding of statistics, linear algebra, and probability theory.
You can perform your NLP tasks using frameworks. But math helps you understand the data and choose the right algorithms.
Here are some resources you can use to learn math:
- Freecodecamp’s linear algebra — full college course
- Khan academy’s linear algebra course
- Khan academy’s multivariable calculus course
- Coursera’s mathematics for machine learning specialization
3. Machine Learning
Machine learning is a hot field and has applications in many fields such as health, education, and finance. In a nutshell, it is a subfield of AI that tries to create models that learn from data. You can use this field to find hidden pattern in your data.
Before developing deep learning techniques, algorithms such as logistic regression and support vector machines were widely used in this field. These algorithms are still used today. As a rule of thumb, if you’re working with small data, you can take advantage of machine learning algorithms.
Here are some resources you can use to learn machine learning:
- Coursera’s supervised machine learning: regression and classification
- Coursera’s machine learning specialization
- Freecodecamp’s machine learning for everybody
- Codebasic’s machine learning for beginners
- Principles of machine learning Python
4. Deep Learning
Nowadays, deep learning techniques are very popular. We came across a new deep learning technique almost every day. So, what actually is deep learning?
Deep learning is a subfield of AI that enables you to extract hidden pattern in big data. Deep learning models is a game changer in AI. Many unsolved problems such as image classification and language translation have been solved with techniques in this field.
There are many deep learning techniques you can use in NLP. The most widely used of these techniques are models based on the Transformer architecture.
Before Transformers, architectures based on Recurrent Neural Networks (RNN) were used. However, these architectures struggled with processing long texts due to vanishing gradient problem. This is where the transformers come into play.
Transformers: Revolutionizing NLP
Transformer is a neural network architecture introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. It has revolutionized the field of NLP and achieved state-of-the-art results in various NLP tasks.
It breaks the text into smaller pieces called tokens and converts them into numerical representations for the algorithm to understand. Then, it attempts to comprehend the relationships, contexts, and meanings within the texts using these mathematical representations.
Unlike traditional language models, the Transformer utilizes an attention mechanism that captures connections between words in long texts. It considers both the sequence and position of words in a sentence. This approach has shown remarkable success in tasks such as language translation, text generation, and text classification, reaching human-level performance.
There are two important models based on the Transformer architecture: BERT and GPT. BERT is primarily used for classification tasks, while GPT excels in text generation. Learning these models is crucial for NLP projects.
HuggingFace is a Game Changer in NLP
Training these models requires substantial amounts of data and computational power. However, the good news is that you don’t have to train these models from scratch. Hugging Face provides pre-trained models that you can download and fine-tune according to your specific data.
For example, you can fine-tune the recently released Falcon model on a single GPU with at least 14 GB of memory on your own machine. Falcon has demonstrated superior performance compared to other state-of-the-art models from Google, DeepMind, LLaMa, and others, according to the OpenLLM Leaderboard.
If these terms sound weird, welcome to the world of NLP. This field is dynamic, with new models emerging almost every day. Remember, continuous learning is essential to keep up with these developments.
- Dive into Deep Learning
- Neural Networks and Deep Learning by Michael Nielsen
- Codebasics deep learning with Tensorflow
- MIT’s introduction to deep learning
Now, let’s explore the libraries and frameworks you can use for NLP.
Frameworks
There is no need to reinvent the wheel, right? Frameworks allow you to use ready-made code instead of coding from scratch. There are numerous frameworks and libraries available for NLP. Let’s go through some of them.
NLTK
One of the most well-known libraries in NLP is NLTK (Natural Language Toolkit). If you’ve worked with text data, you’ve probably heard of this library. It is a leading platform for building NLP projects to work with natural language.
NLTK provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet. It also offers a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
SpaCy
Another powerful library in NLP is SpaCy. It is an open-source Python library that helps you build real products.
SpaCy is king for extracting knowledge from large-scale information. For example, if your application needs to handle all web dumps, SpaCy is perfect to do this as it is written in Cython.
The good news is that with the release of version 3.0, SpaCy now also supports Transformer-based models.
Gensim
Another library I would like to mention is Gensim. It is free Python library that helps you train large-scale semantic NLP models, represent text as semantic vectors, find semantically related documents. It also is the fastest library to train vector embeddings.
It allows you to run on Linux, Windows and OS X, as well as any other platform that supports Python and NumPy.
The Gensim community also offers pretrained models for specific domains such as legal or health, via the Gensim-data project.
Transformers
As mentioned earlier, the Transformer architecture plays a significant role in NLP. Hugging Face has developed a library called Transformers, which focuses on language models.
You can easily utilize powerful models like BERT, GPT, and RoBERTa using this library. With these models, you can tackle various NLP tasks such as text classification, text generation, translation, sentiment analysis, and more.
This library enables you to work with PyTorch, TensorFlow, and JAX. This helps you use a different framework at each phase of a model’s life; build a model in one framework, and then load it for inference in another.
Others
In addition, you can take advantage of TensorFlow, or PyTorch to train your own models or fine-tune existing models.
TensorFlow is commonly used in industry for end-to-end machine learning projects, while PyTorch is popular in academia for research purposes.
Nice, we’ve seen some of the libraries and frameworks you can use for your NLP projects. Now let’s move on to how to learn this field.
Books for Mastering NLP
There are many resources for learning NLP. In this section, I’ll recommend some books that can help you master this area.
Practical Natural Language Processing
The first book I would recommend is “Practical Natural Language Processing”. This book focuses on practical NLP topics and provides examples and code in Python. It covers various NLP applications using machine learning and deep learning techniques, including their use in healthcare, social media analysis, and marketing.
Natural Language Processing with Transformers
The next book I would recommend is “Natural Language Processing with Transformers”. As mentioned earlier, Transformers have had a significant impact on deep learning for NLP. This book explores the use of the Transformers library developed by Hugging Face. It guides you through understanding how Transformers work and integrating them into your applications.
Transformers for Natural Language Processing
Another book I would recommend is “Transformers for Natural Language Processing”. This book covers Transformer architectures developed by major companies such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. It covers the original Transformer architecture before diving into natural language understanding, generation, and pre-trained models.
These books can provide valuable insights and practical knowledge to help you excel in NLP. However, remember that the field of NLP is rapidly evolving, so staying updated with the latest research papers and resources is also essential.
Apart from books, there are numerous free online resources available to learn NLP. YouTube is a great platform for video-based learning, and Udemy offers many courses on NLP. Medium is also an excellent platform for text content, where you can find various NLP articles and tutorials.
Wrap-Up
NLP has become a very popular field in recent years with the discovery of new approaches such as Transformer architecture. However, it has many challages to learn this field.
In this blog, I provided a roadmap for learning NLP. First, learn a programming language like Python, and be familiar with the fundamentals of mathematics, linear algebra and statistics, then delve into machine learning and deep learning. Once you’ve acquired these skills, get your hands dirty with libraries such as Transformers, TensorFlow, and PyTorch using the dataset in Kaggle.
Remember, NLP is a dynamic field with new models and techniques emerging almost every day. So, Learning to learn is crucial in this field.
That’s it. Thanks for reading. Let’s connect YouTube | Twitter | LinkedIn.
