Roadmap to Master NLP in 2022
This article was published as a part of the Data Science Blogathon.
A few days ago, I came across a question on “Quora” that boiled down to: “How can I learn Natural Language Processing in just only four months?”. Then I began to write a brief response. Still, it quickly snowballed into a detailed explanation of the pedagogical approach I employed, and by using that approach, how I made the transition from a Mechanical Engineering nerd to a Natural Language Processing (NLP) enthusiast.
This article will discuss the complete Natural language Processing (NLP) Roadmap for beginners. It is going to be a bit different concerning other articles.
One of the reasons beginners get confused when learning NLP is that they don’t know what to learn from where and how? There are just too many options for courses, books, and NLP algorithms.
I will share a set of steps that you should take to master NLP.
Image Source: Link
Let’s first understand, What NLP is?
Natural Language Processing (NLP) is the area of research in Artificial Intelligence that mainly focuses on processing and using text and speech data to create intelligent machines and create insights from the data.
Prerequisites to follow the Roadmap effectively
👉 Basic Idea of Python programming language.
👉 Simple Idea of Machine and Deep Learning algorithms.
Libraries used while following the Roadmap
👉 Natural Language Toolkit (NLTK),
👉 Core NLP,
👉 Text Blob,
👉 Pattern, etc.
Let’s get started Step-by-Step
Text Preprocessing Level-1
👉 Parts of Speech (POS),
👉 Stopwords removal,
👉 Punctuation removal, etc.
In NLP, we have the text data, which our Machine Learning algorithms cannot directly use, so we have first to preprocess it and then feed the preprocessed data to our Machine Learning algorithms. So, In this step, we will try to learn the same basic processing steps which we have to perform in almost every NLP problem.
Advanced level Text Cleaning
👉 Correction of Typos, etc.
These are some advanced-level techniques that help our text data give our model better performance. Let’s take an advanced understanding of some of these techniques straightforwardly.
Normalization: Map the words to a fixed language word.
For Example, Let’s have words like b4, ttyl which, according to human beings, can be understood as “before” and “talk to you later” respectively. Still, machines cannot understand these words the same way, so we have to map these words to a particular language word. This map is known as Normalization.
Correction of typos: There are a lot of mistakes in writing English text or for other languages text, like Fen instead of a fan. The accurate map necessitates using a dictionary, which we used to map words to their correct forms based on similarity. Correction of typos is the term for this procedure.
NOTE: These are only some of the techniques I described, but you have to update your knowledge by learning different methods regularly.
Text preprocessing Level-2
👉 Bag of words (BOW),
👉 Term frequency Inverse Document Frequency (TFIDF),
👉 Unigram, Bigram, and Ngrams.
All these are the primary methods to convert our Text data into numerical data (Vectors) to apply a Machine Learning algorithm to it.
Text preprocessing Level-3
👉 Average word2vec.
All these are advanced techniques to convert words into vectors.
Hands-on Experience on a use case
After following all the above steps, now at this step, you can implement a typical or straightforward NLP use case using machine learning algorithms like Naive Bayes Classifier, etc. To have a clear understanding of all the above and understand the next steps.
Get an advanced level understanding of Artificial Neural Network
While going much deeper into NLP, you do not take Artificial Neural Network (ANN) very far from your view; you have to know about the basic deep learning algorithms, including backpropagation, gradient descent, etc.
To complete this step, we have to gain the basic knowledge of Deep learning, mainly artificial neural networks.
Introduction to Deep Learning and Neural Networks
Optimization Algorithms for Deep Learning
Deep Learning Models
👉 Recurrent Neural Networks (RNN),
Link to YouTube video: https://youtu.be/UNmqTiOnRfg
👉 Long Short Term Memory (LSTM),
👉 Gated Recurrent Unit (GRU).
RNN is mainly used when we have the data sequence in hand, and we have to analyze that data. We will understand LSTM and GRU, conceptually succeeding topics after RNN.
Text preprocessing Level-4
👉 Word Embedding
👉 Word 2 Vec
Now, we can do moderate-level projects related to NLP and make pro in this domain. Below are some steps which will differentiate you from other people who have also worked in this field. So, to take an edge over all those people learning these topics are a must.
👉 Bidirectional LSTM RNN,
👉 Encoders and Decoders,
👉 Self-attention models.
Fig. Seq2Seq model: Used in Language translation
Image Source: link
Link to the Video: https://youtu.be/qqt3aMPB81c
The Transformer in NLP is an architecture that seeks to handle sequence-to-sequence tasks while handling long-range relationships with ease. It leverages self-attention models.
👉 BERT(Bidirectional Encoder Representations from Transformers)
It is a variation of the transformer, and it converts a sentence into a vector. It is a neural network-based technique used for natural language processing pre-training.
This completes the Roadmap to becoming an NLP expert in 2022!
Now, let’s move to the most exciting part of this article, i.e., what all resources you have to follow to learn the topics mentioned above. So, keeping the above issues in mind, I have created a complete blog series of NLP in a detailed manner.
This blog series contains practice questions of topics covered in each blog. Also, this series includes 2-3 projects related to NLP which you have to try to take a deep understanding of all the topics in a detailed manner. So, follow the mentioned resource and become an NLP expert quickly.
Analytics Vidhya Complete Blog Series to learn all the mentioned topics of NLP (Resources)
Part 2: Some basic knowledge Required to Learn NLP
Part 3: Understanding about Text Cleaning and Preprocessing
Part 4: Learning Different Text Cleaning Techniques
Link to YouTube video: https://youtu.be/BY1JD4SPt9o
Part 5: Understanding Word Embedding and Text Vectorization
Link to YouTube video: https://youtu.be/ERibwqs9p38
Part 7: Detailed Discussion on Word Embedding
Part 8: Most Important NLP Tasks
Part 9: Basics of Semantic Analysis
Part 10: What is Named Entity Recognition
Link to YouTube video: https://youtu.be/9qz1yEQlVhg
Part 11: Basics of Syntactic Analysis
Part 12: Need of Grammar in NLP
Part 13: What and Why Regular Expressions
Part 14: Detailed discussion on Topic Modelling
Link to YouTube video: https://youtu.be/DDq3OVp9dNA
Part 15: Topic Modelling with the help of NMF
To understand this blog, do you have an idea of what SVD is? So, to learn that you can refer to the following video lecture.
Link to YouTube video: https://youtu.be/mBcLRGuAFUk
Part 16: Topic Modelling with the help of LSA
Part 17: Topic Modelling with the use of pLSA
Part 18: Topic Modelling with the help of LDA (Approach-1)
Part- 19: Topic Modelling with the help of LDA (Approach-2)
Part 20: Basics of Information Retrieval
Thanks for reading!
I hope that you have enjoyed the article. If you like it, share it with your friends also. Something not mentioned or want to share your thoughts? Feel free to comment below, And I’ll get back to you. 😉
If you want to read my previous blogs, you can read Previous Data Science Blog posts from here.
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