Have you ever used autocorrection while writing emails or autosuggestion of replies on Linkedin? If your answer is “yes”, you can thank natural language processing (NLP). Similarly, we come across so many uses of NLP in our daily work that make our life easy. Do you want to learn the skills on which these tools are built and get into this amazing field of NLP which makes machines understand the human language along with the context?
One of the reasons students get confused about NLP is that they don’t know what topics they actually need to focus on and from where they can learn these topics.
In this article, we will learn what are all the topics you need to learn in NLP in a step-wise order (from basics to advance), the demand of NLP, what are the prerequisite skills, and its important libraries.
What is Natural Language Processing?
Natural Language Processing (NLP) is a branch of artificial intelligence that deals with textual and speech data. It is the technology that is used by machines to understand, analyze, and process human language and extract important useful information from it.
Application of NLP
Natural language processing has a wide range of applications. Below is a list of 10 applications, but the list is beyond that.
- Chatbots
- Smart Replies in Chats (Email or Linkedin)
- Virtual Assistant in Customer Care Services
- Social Media – Removing Adult Content
- Spell Checking and Grammer Correction
- Correct Information Retrieval in Search Engines like Google
Prerequisite to Learn NLP
Below are the important skills you need to have if you want to learn concepts of NLP.
- Python Programming Language
- Basic Machine Learning Algorithms
- Knowledge of neural networks if you want to use deep learning models like RNN, and LSTM.
Components of NLP
There are mainly two main components of Natural Language Processing: NLU and NLG.
1. Natural Language Understanding (NLU)
Machines first need to understand the data before processing it. Natural language understanding is a subfield of natural language processing that provides machines with the ability to understand human language.
2. Natural Language Generation (NLG)
Natural language generation gives machines the ability to produce natural language both spoken and written that makes sense to humans.
Natural Language Processing Skills
Learning NLP is not a difficult task. You just need to follow the below steps which take you from basics to advanced and you will definitely cover everything to become job ready.
Step 1. Text Preprocessing -1
The data available cannot be directly used in its raw form as an input to machine learning models and therefore need to be processed. Text processing includes various steps to clean the data. Below are a few text-cleaning steps you will need to perform in all the NLP projects.
- Stemming or Lemmatization
- Removing Stopwords
- Removing Punctuations
- Tokenization
- Part of Speech
- Regular Expression
Step 2. Text Preprocessing – 2
After cleaning the data, the next step is to convert the textual data into a format required by machine learning algorithms. Hence text data is converted into a tabular format which has important words as features and their occurrence as values. Below are a few concepts you need to learn.
- Bag Of Words (BOW)
- Term Frequency Inverse Document Frequency (TF-IDF)
- Unigrams, Bigrams, and Ngrams
These are the techniques that create vectors based on the frequency of occurrence of words in the text or document.
Step 3. Word Embeddings
Word Embeddings are the numerical representation of textual data. These are the advanced techniques to convert words into vectors along with their contextual information.
- Word2Vec
- Average Word2Vec
- Glove
Step 4. NLP Libraries
There are many python libraries that are capable of performing various tasks in NLP like named entity recognition, sentiment analysis, tokenization, etc. Below are a few important libraries that can help you with NLP tasks:
- NLTK
- spacy
- Gensium
- TextBlob
- Huggingface
Step 5: Basic Projects in NLP
After completing step 4, you should work on simple projects which use the skills learned. It will clear your concepts learned so far. Naive Bayes is a popular machine-learning algorithm used for text processing. It can be used to perform the below tasks.
- Text Classification
- Sentiment Analysis
- Topic Modelling
- Spam Classification
Till Step 5, we were majorly dealing with machine learning. Now it’s time to jump into deep learning. The prerequisite for it is the knowledge of Artificial Neural Networks (ANN) and concepts like backpropagation, gradient descent, etc.
The major difference between the techniques learned till now and the neural networks (next topic) is that neural networks also consider the sequence information of the text which wasn’t kept by techniques like (BOW, TFIDF, or word embeddings).
Step 6. NLP for Deep Learning
There are various deep-learning algorithms that can be used for the advanced processing of textual or speech data. The basic data cleaning steps for NLP projects in deep learning are the same as we learned in the initial steps. Below is a list of neural networks from basic to advanced.
- RNN
- LSTM
- Encoder-Decoder
- Bidirectional RNN and LSTM
- Seq-to-Seq Models
Step 7. Advanced Topics in NLP
Below are the topics that will differentiate you from the other people who are also working in this field. These models are new in the market and their usage is widely booming in the NLP market.
- Attention Models
- Transformer
- Bert (Bidirectional Encoder Representations from Transformers)
These models are capable of handling the long-term relationship in the textual data with ease and overcoming the drawbacks of the old deep learning models.
Step 8. Advanced Projects in NLP
Now as you have become a champion in natural language processing. It is a good time to utilize the skills learned and implement a few amazing projects.
- Chatbot or Question Answering bot
- Text Summarization
- Text Generation
- Language Translation
The projects created in this series of learning NLP are capable enough to be used in your resume while applying for jobs.
NLP Engineer Career FAQs
Below are a few questions you may be having in your mind if you are planning to move into the field of natural language processing.
1. Who is an NLP Engineer
NLP Engineers are responsible for programming techniques to process and analyze everyday human language. They provide machines the ability to understand the language along with the context and generate the possible output in a human-understandable format.
2. Education Requirements for an NLP Engineer
The above-mentioned skills are more important when compared to a degree. You can easily learn NLP by following a step-wise process regardless of the degree you have. But there is no doubt that having a background in programming can make the process easy.
3. Demand for NLP
There is a lot of textual data around us like the data on google, offline documents, economical data, financial data, patient data, reviews, etc. These data can include information that can be very useful for many companies. Due to this wide range of applications, the demand for natural language processing techniques has increased in past few years. The industry demand for NLP engineers has never been so high and it is expected to increase exponentially in the next few years.
4. How Many Days it will Take to Learn NLP
The amount of time it takes to learn NLP depends on the current skillset you have. If you have no idea about programming and machine learning. Then first you will need to learn those skills which are already covered in How Long Does it Take To Learn Data Science From Scratch. After having these skills learning the basics of NLP will take 2-3 months (Step 1- Step 5). Learning neural networks and deep learning for NLP (Step 6- Step 8) will take 3-4 months more. Hence covering the whole NLP topics can take around 6-7 months.
5. How Difficult is it to Learn NLP
To be honest, learning basic NLP is very easy. But learning the advanced NLP is comparatively challenging, but the outcomes you see with deep learning especially Transformers and Bert are mindblowing.
End Notes
The task of predicting something in the field of text or speech has revolutionized in the past few years. It has impacted many industries. For example, the SEO (Search engine optimization) process is changed in google search, bots have replaced customer care, blogs are written using text generators, and teachers are creating questions and answers by using NLP sources and whatnot.
This motivates you to be a part of the NLP community as it has great career opportunities ahead. In this article, I have tried to cover all the queries you could have about NLP. If you still have more queries in your head, feel free to ask any doubts or give your feedback in the comment section below.
Happy learning!
