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4 Data Related Books I’ll Be Reading In April

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Photo by Susan Q Yin on Unsplash
Photo by Susan Q Yin on Unsplash

Books are truly my lifeblood; I’d consider myself an avid book worm, but I must retract that statement whenever it comes to technical books. For some strange reason, technical books always make me feel more sleepy rather than interested.

However, in the past, I’ve managed to conjure up the strength to keep my eyelids open and get through these books. Something I’ve realized is that whenever I have done that, I’ve made massive progress in the subject or area I’m studying, hence this month I’ll be doing it again.

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For those that are interested in following what books I am reading throughout the year, check out my Instagram page which is dedicated solely to giving my views about different books I’m reading.


1 Practical Natural Language Processing: A Comprehensive Guide To Building Real World NLP Systems

Source: Amazon Book Page
Source: Amazon Book Page

This book was given to me quite a while back by Phillip Vollet – A very popular NLP evangelist on LinkedIn (Give him a follow, you won’t regret it) – but I was reluctant to open it due to my phobia of technical books.

He mentioned that this book is one of the best guides for learning to build, iterate, and scale NLP systems in a business setting and tailor them for a particular industry verticle.

2 Introduction To Information Retrieval

Source: Amazon Book Page
Source: Amazon Book Page

I really can’t remember who suggested this book to me, but someone must-have – the reason I know this is because the book has been sitting in my Amazon basket for months and I typically do that when I hear someone mention a book I find interesting during a conversation. I’m not too sure what this book has to offer, but I’ve seen very good reviews online so I am looking forward to diving into this one.

This is what the book description states on Amazon:

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book’s supporting website to help course instructors prepare their lectures.

3 Transformers For Natural Language Processing

Source: Amazon Book Page
Source: Amazon Book Page

For anyone that joins in on Harpreet Sahota‘s office hours – every Friday at 4.30 PST – then you would be familiar with a certain Thom Ives who cannot stop rambling on about this book. Thom has done so well for himself in his career in data, but he undoubtedly earned my respect when he shared that for every mistake he made in his Data Science career, he grew a strand of grey hair – It may not make sense now, but check out his LinkedIn and you’ll understand why this moved me.

Transformers are currently the state-of-the-art architecture for many Natural Language Processing tasks, like Machine Translation. I’ve dabbled around with them a little bit with the HuggingFace framework, but I’m sure there’s no harm in taking a deeper dive into them.

4 Machine Learning Engineering

Source: Amazon Book Page
Source: Amazon Book Page

Some of you may remember Andriy Burkov, the author of The Hundred-Page Machine Learning book. He’s back with another one. This book is said to be filled with nuggets from the best practices to the design patterns of building reliable machine learning solutions that scale. Anything that is going to help me improve building ML solutions that scale is going to get my attention and this time it just so happened to be a trusted source in Andriy Burkov.

Here’s what Chief Decision Scientist at Google, Cassie Kozyrkov, had to say about this book:

"You’re looking at one of the few true Applied Machine Learning books out there. That’s right, you found one! A real applied needle in the haystack of research-oriented stuff. Excellent job, dear reader… unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won’t be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book. This one is different"

Final Thoughts

In Q1, I dedicated most of my reading to biographies about some people I find inspirational. For Q2, I aim to focus much more of my attention on books much more related to my field that will help me to develop and improve as a Data Scientist. Once again, if you’re interested in following my reading journey, follow my Instagram.

Thank you for reading! Connect with me on LinkedIn and Twitter to stay up to date with my posts about Data Science, Artificial Intelligence, and Freelancing.

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