Top 5 Must-Read Statistics Books For ML Enthusiasts
Machine learning cannot be limited to one subject due to its widespread applications, ranging an adoption in a variety of disciplines including science and engineering. It mainly derives its core concepts from mathematics and statistics. When dealing with data, knowledge of statistics definitely comes in handy. ML is about encountering data on a regular basis and uses one or the other form of statistical method to understand it. In fact, there is a definite overlap between these two fields. The difference only lies in the strategies and procedures followed in ML and statistics. Nonetheless, mastering statistics is beneficial in comprehending ML methods. But, when it comes to absorbing statistics, some may find it difficult to understand the concepts involved and can be intimidated by the diverse concepts. This article showcases top books on statistics that dissolve difficult topics into easier and more interesting themes for the reader. The books are listed in a random order and can be read without any apprehension regarding technicalities involved in the subject. 1. Statistics For Management by Richard Levin And David Rubin Image courtesy : Goodreads This classic, no-nonsense book on statistics follows a business-oriented approach, where Levin and Rubin explain the concepts in an easy to understand manner followed with real world examples in each chapter to show the practicality of these concepts.The book also includes learning aids such as review exercises, concepts tests among others. In addition, working with statistical tools on software packages such as MS-Excel and SPSS is also presented at the end.The book is even adopted by many universities as part of graduate and undergraduate level coursework on statistics. A must-read for total beginners in statistics. You can buy the book here. 2 . Naked Statistics — Stripping The Dread From The Data by Charles Wheelan Image courtesy : Amazon.com In this book, the author Charles Wheelan has transformed the way statistics is perceived. He brings in real-life examples of companies which have leveraged large benefits from statistical intuition. The book does not stick to the intricate details in the subject and presents a simpler, effective representation of topics. Wheelan focuses on important topics such as regression analysis, inference and correlation among others to emphasise how crucial data can be manipulated by entities such as organisations and even political parties. This book is a must read for anyone interested to know how statistics works its way in the real world. You can buy the book here. 3. Statistics Done Wrong: The Woefully Complete Guide by Alex Reinhart Image courtesy : Amazon.com What happens when statistical methods in research fail? Well, this book answers the question. Alex Reinhart describes the negative outlook in statistics that scientists and researchers still follow today, and the implications it has caused along the process. He presents examples of stats gone wrong and problems evident in the field. Primarily focussed on statistical research, this book advises the right approach and nuances to be followed when conducting a top-down research or experiment, with picturesque examples. For instance, be it insights on designing the right experiment or selecting the best statistical analysis method, the information is right there. This book is for anyone aspiring to be a well-informed statistician or a data scientist. You can buy the book here 4. The Lady Tasting Tea: How Statistics Revolutionised Science In The Twentieth Century by David Salsburg Img courtesy: Macmillan Publishers A slight offbeat from the list, this book offers philosophical perspectives of many statistical methods. The book begins with the famous Lady Tasting Tea experiment observed by statistician Ronald Fisher, and then proceeds with many subsequent concepts that revolutionised the subject of statistics. The author David Salsburg provides a witty take on these concepts along with relishing fascinating examples in the journey of statistics. This book is suggested for anyone willing to begin statistics on a high note. You can buy the book here. 5. The Cartoon Guide To Statistics by Larry Gonick And Woollcott Smith Image courtesy : Amazon.com This primer book presents statistical concepts in a comical-style theme. The concepts are illustrated in the form of cartoons for quick understanding of the underlying phenomenon, with a humorous take. The authors bring out the content in such a way that anyone with no prior knowledge of statistics will be able to make distinctions between various ideas in the subject. The book covers all important aspects of modern statistics right from presenting and organising data to realising tough-to-digest topics such as Central Limit theorem, confidence intervals, estimation and many more. Although, notations may slightly differ from other standard textbook-level statistics, this book is suggested for anyone who wishes to take statistics to next level with a solid understanding of basic concepts. You can buy the book here. Conclusion The books mentioned above will give a solid foundation in the field of statistics. On top of this, they serve as a beacon for those starting with ML as well. Basically, statistics and ML work with data for problem-solving. With a sound knowledge on the former, the path of learning and working with ML will be much easier.
Machine learning cannot be limited to one subject due to its widespread applications, ranging an adoption in a variety of disciplines including science and engineering. It mainly derives its core concepts from mathematics and statistics. When dealing with data, knowledge of statistics definitely comes in handy. ML is about encountering data on a regular basis […]
This article showcases top books on statistics which dissolve difficult topics into easier and interesting themes for the reader.
["machine learning books", "Statistics Machine Learning"]
["Go", "machine learning", "machine learning books", "programming_languages:R", "AI", "ML", "programming_languages:Go", "RAG", "Statistics Machine Learning", "GAN", "R"]
["AI", "machine learning", "ML", "RAG", "R", "Go", "GAN", "programming_languages:R", "programming_languages:Go"]
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