This blog is a step-by-step guide for a beginner in NLP. If you are someone who wants to know what is the best way to learn NLP from scratch, then please go through our blog till the end. We assure you will build the confidence and gear up yourself to make a career transition into data science as an NLP Engineer.
We will first begin with what are the essential subjects one must be aware of, to prepare them for diving into the world of Natural Language Processing.
Before we start with the jargon of prerequisites, please remember that the first and foremost thing you need to have is a strong desire to learn NLP. If you have that with you, you may rely on our article and follow the steps patiently to climb up the ladder of becoming a successful NLP engineer.
NLP stands for Natural Language Processing. It is the study of human’s natural language by using computational techniques. It is a subdomain of Artificial Intelligence that has recently gained popularity because of its amazing applications like Chatbots, Sentence Autocompletion, Text Summarisation, etc. For getting started to build such exciting NLP applications, a specific set of skills is required which has been highlighted by the points mentioned below.
Since computational methods are used in NLP, it becomes obvious that you must know graduation-level mathematics. That will include advanced calculus, linear algebra, probability and statistics, and differential equations. It is needed to understand machine learning and deep learning algorithms that are used along with NLP techniques.
Ability to code in one of the popular programming languages like C/C++, Python, R, Java.
Additionally, an adequate knowledge of linguistics will work like a charm if you want to understand NLP methodologies deeply.
A computer! Duh!
We understand that there is a possibility that you are a student who is yet to complete their graduation. Don’t worry if that is the case. In the following few sections, we will design a beginner-friendly learning path that anyone can pursue to start their journey towards becoming an NLP engineer. And if you are an intermediate learner, you can use our article as a checklist and figure out what you need to learn. So, let’s get started!
You have now entered the section where we will explain in detail what is the best method of learning NLP. We will first shine some light on all the subjects and topics you must know for NLP and book recommendations for each one. Next, we will suggest ways to enhance your knowledge to become capable of solving real-world problems in NLP.
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Post-pandemic days, one of the most common requests we get is how to learn NLP at home? Fortunately, NLP is not a subject that requires fieldwork. So, you can learn it at home easily. Allow us to now answer the questions of how you can achieve that.
Advanced Calculus
Yes, it is time to recall Sir Issac Newton, who gifted the world a tremendous mathematical power to solve complicated problems through Calculus’s invention. We know a few of you might believe Gottfried Leibniz invented it but right now, let us focus on where you can learn it from.
Recommended Book: Calculus (2002) by Monty J. Strauss, Gerald L. Bradley, and Karl J. Smith
About the Book: The book is pretty amiable for beginners in calculus. It starts with basic concepts in mathematics that are usually introduced at the high school level, like functions, graphs, limits, differentiation, etc., and then gradually raises the bar by diverging to multiple variable integration and vector analysis. We suggest you complete at least the first fourteen chapters from this book and make sure to solve 50% of the exercises after each chapter.
Linear Algebra
Until high school, linear algebra seemed so easy because we thought linear algebra was all about matrices and determinants. But as one starts pursuing graduation, they realize linear algebra is so much more than that and is pretty complicated.
Recommended Book: Advanced Engineering Mathematics, 10ed, by Erwin Kreyszig
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About the Book: It contains two chapters (7 and 8) that have the complete linear algebra syllabus required for understanding NLP. It will first help you recall the basics of matrices and determinants first. After that, you will get to explore vector spaces, eigenvectors, and eigenvalues.
Probability and Statistics
Probability and statistics are two topics that go hand in hand. You can not study one without the other. We have done a detailed blog that will serve as the Ultimate Guide to Statistics for Machine Learning Beginners. But, if you are looking for one book that contains almost everything for probability and statistics, then the one recommended below will be our pick.
Recommended Book: Probabilistic Machine Learning by Kevin Murphy
About the Book: This is an excellent book for learning probability and statistics for machine learning. That is because it has all three of them with the right amount of depth. It starts with the basic idea of machine learning to build motivation for the readers and then introduces probability concepts with many compelling examples. Finally, it moves on to statistics, information theory to lay down the perfect beginner-friendly path for machine learning algorithms.
Recommended Reading: 50 Statistic and Probability Interview Questions for Data Scientists
Differential Equations
Now, differential equations are not difficult to understand because only a handful of those we can solve algebraically. So, this will be probably the fastest that you’ll learn from this list.
Recommended Book: Mathematical Methods in the Physical Sciences by Mary L Baos
About the book: If you go by the title, this book might seem like a physics textbook but trust us, it has all the mathematics you need to learn to learn differential equations. It is a complete mathematics book, and you can use it to learn all the previously mentioned topics (except probability and statistics). You don’t need to know physics for this textbook and once you start reading it, you will realize it is one of the best graduation-level mathematics textbooks ever.
Machine Learning and Deep Learning Algorithms
After you are done with the mathematics, you will be all set to take the next giant leap to learn machine learning and deep learning algorithms. For this, we will suggest to you more than one book because these algorithms play a key role in building NLP-based applications.
Book #1: An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani
Book #2: Machine Learning by Tom Mitchell
Book #3: Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
About the Books: The three books that we have mentioned above have to be read in order. The first one gives a simple introduction to machine learning algorithms, and the next one by Tom is an advanced book. In addition to these two, you should refer to the third book, which is freely available online and you may download the Deep Learning book here.
Recommended Reading: 15 Amazing Deep Learning Project Ideas to Boost Your Resume
Programming Language
If you know one popular programming language already, this step won’t take a lot of your time because working with different languages becomes easy. However, if you are a beginner and don’t know which language you should start, we recommend starting with Python because it is easy to learn and widely used.
Book: Python for Absolute Beginners by Mike Dawson and Michael Dawson
About the Book: This is one of the most enjoyable books you’ll ever read to learn Python. It starts from the basic and as the title suggest, it is for absolute beginners who have zero knowledge about Python programming language. The author makes sure to make the journey of learning Python of a reader entertaining through his writing style. The book also has terrific examples that are available online on GitHub.
Linguistics Methodologies used in NLP
Often NLP learners skip this subject because they believe it is not precisely related to NLP and is least important. However, understanding linguistic methodologies is crucial because it will assist you in knowing the nuts and bolts of NLP algorithms quickly and deeply.
Book: Linguistic Fundamentals for Natural Language Processing by Emily M. Bender
About the Book: This is a good book for NLP beginners as it doesn’t start with the technicalities of linguistics and focuses first on building the motivation for the reader to learn the subject. After fueling the reader with the necessary inspiration, it dives into the jargon with Morphology, Morphonology, syntax, and parts of speech followed by semantics.
NLP Techniques
Finally, you have reached the last subject on this list. After you have finished reading the above-mentioned books, you will be all set to dive into the world of NLP. And the first step towards learning NLP techniques will be reading the two books mentioned below.
Book #1: Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin.
Book #2: Foundations of Statistical Natural Language Processing Christopher D. Manning and Hinrich Schutze
About the Books: The two books are our best pick for learning NLP. They introduce linguistics basics so you can have a quick revision and then move ahead to the jargon used by NLP research engineers like collocations, N-gram models, Hidden Markov Models, POS-Tagging, etc.
Recommended Reading: Top 50 NLP Interview Questions and Answers for 2021
You must be knowing already that your knowledge about a subject will be considered incomplete if you haven’t tried enough problems around it. You may enroll in any of the NLP-related courses available online, but hardly any of them will introduce you to industry-relevant problems and their solutions. If you are genuinely interested in making it big in the NLP world, try your hands at the simple NLP projects ideas mentioned below.
These days, many eCommerce companies are presenting statistics of customer reviews who have purchased a specific product to lure other customers. They are able to achieve this using NLP methodologies along with machine learning algorithms. Try out a project that works on a customer review dataset from an eCommerce website like Amazon and apply NLP and Machine learning techniques to analyse each product review’s sentiments (good or bad or neutral).
Complete Solution: Ecommerce product reviews - Pairwise ranking and sentiment analysis
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Most of us receive so many advertisements through emails every day and occasionally mark a few irrelevant ones as spam. After this, we observe that those types of emails are automatically diverted to the spam folder and never make it to our inbox. That is primarily because of an NLP-based spam classification system running at the backend. This project aims to build a spam classifier that reads the contents of input emails and learns how to classify them as “spam” and “not spam” using NLP and Machine learning techniques.
Complete Solution: Spam Classification Project| Machine Learning for Beginners -
Recommended Reading: Text classification in NLP
Chatbots, Chatbots everywhere, making the customer care experience lit!
By the above quote, we want to highlight how chatbots have revolutionized the customer care experience of many companies. Chatbots are AI-based conversation machines that engage with customers and try to resolve customer issues on their own until human intervention is essential. And the exciting part about them is that most of them rely on NLP and machine learning algorithms for their implementation. So, go ahead and try to build a chatbot today by referring to the solution below.
Complete Solution: NLP chatbot example application using python
Recommended Reading: 15 NLP Projects Ideas for Beginners With Source Code for 2021
To become an expert in Natural Language Processing Applications, one has to thoroughly understand different machine learning and deep learning algorithms like decision trees, neural networks, transformers, etc. You might find the theory around them quickly, but you need to develop industry-relevant skills by working on different projects that utilize them. If you are looking for such projects, check out the links below to know more.
Deep Learning Projects Ideas for Beginners | Deep Learning Projects
Neural Network Projects for Beginners
1. Is NLP easy to learn?
Yes, NLP is easy to learn as long as you are learning it from the right resources. In this blog, we have mentioned the best way to learn NLP. So, read it completely to know about the informative resources.
2. How long does it take to learn NLP?
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.
3. How do I start learning NLP?
You can learn NLP by working on industry-relevant solved projects by ProjectPro. ProjectPro dashboard offers a customized learning path depending on your experience level that you can use to learn NLP.