![]() Overall, a great book for beginners as well as advanced users. The book will help you through the process of setting up the required software until the creation, update, and monitoring of models. Though the book covers the basics of Python, you might want to start the book after you gain some basic knowledge of Python. ![]() which beautifully adds to the reading experience. The author shares his experiences in the various areas of ML such as ad optimization, conversion rate prediction, click fraud detection, etc. The book gets you started with Python and machine learning in a detailed and interesting way with some classy examples like the spam email detection using Bayes and predictions using regression and tree-based algorithms. Python Machine Learning By ExampleĪs the name says, this book is the easiest way to get into machine learning. However, reading this book alone won’t be sufficient as you get deeper into ML and coding. This book is for beginners and covers basic topics in detail. The book has examples in Python but you wouldn’t need any prior knowledge of either maths or Programming languages for reading this book. You will get a good grasp of ML concepts. ML is quite a complex topic, however, after practicing along with the book, you should be able to build your own ML models. The tone is friendly and easy to understand. The concepts are explained as if to a layman and with sufficient examples for a better understanding. This is a book that can get you kick-started on your ML journey with Python. Introduction to Machine Learning with Python: A Guide for Data Scientists The book has been one of the most popular books for about 5 decades and that is one more reason why it should definitely be on your bookshelf. If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts. The explanations are pretty neat and resemble real-life problems. ![]() This is perhaps the best book to learn about probability. If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed. This book covers all the topics that are needed for data science. The book also surprises one with a survey of ML models. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc… Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. If you are a beginner, this book will give you a good overview of all the concepts that you need to learn to master data science. Overall a great book to begin your data science journey. ![]() You can find some good real-life examples to keep you hooked on to the book. There are a lot of pictures and graphics and bits on the sides that are easy to remember. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. Just like other books of Headfirst, the tone of this book is friendly and conversational and the best book for data science to start with. Here are some of the best books that you can read to better understand the concepts of data science – Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more. That said, there is nothing better than reading data science books to get the ball rolling. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow. Designing data-intensive applicationsĪpart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. Business analytics – the science of data-driven decision making Head First Statistics: A Brain-Friendly Guide
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