Reading books.

15 Books Every College Student Needs to Read before Graduating

College life is considered a crucial phase of a person’s life. It is the transition period when a student slowly makes his/her way to the professional world. Whatever decisions you make during this period, shapes your future. So, when you are in college, you need to make smarter choices to ensure success. And what better way there can be to get smarter than reading books by some of the best authors of all time?

If you love reading books, you are already a few steps ahead of your friends who don’t practice the same. Earlier there used to be issues with the availability of the books. You needed to visit the library and find the book of your interest. Now you can just download an entire book on your tablet, iPad or Kindle device, and read it anytime you want.

If you are not a big fan of reading, push yourself to read every day. It can not only make you more thoughtful but also allow you to develop your personality. If you don’t know where to start from, here is the list of 15 amazing books that every college student should read.


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Book of the month: Pattern Recognition and Machine Learning review

Our rating: ★★★★☆

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This book is one of my first Data Science books I bought and one of the books I personally use most often. In this Pattern Recognition and Machine Learning review, I will give you my opinion on this book. Most of the core concepts of Data Science are discussed in this book, ranging from Stochastic Gradient Descent to Neural Networks and are explained in clear and understandable English. Some of the courses I followed were hard to follow, but most of the times, I could look up related material in this book which explained it in simple text with many examples. There are also plenty of exercises to help you through a topic and it builds up nicely. It explains nearly all pattern recognition or machine learning concepts and offers an comprehensive introduction to the fields of computer science, data mining and computer vision. Besides that, it sheds also lights on topics like probability theory in which many probability distributions and graphical models are explained. The great thing is that no previous knowledge of pattern recognition is assumed. So for beginners, advanced undergraduates or first year PhD students, students as well as researchers, researchers and practitioners, this book is an absolutely must-have. For more advanced Data Scientist, this book is great reference material. Make sure to buy this book now and add it to your collection!

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