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Probability and Statistics Essentials for Data Science and Machine Learning: 200+ examples and pictures
In the field of data science and machine learning, some areas of probability and statistics require thorough exploration, while others can be briefly touched upon or even omitted. The phrase "Go an inch wide and a mile deep" succinctly captures the approach taken in this book. Rather than spreading too thin and covering a broad range of areas with only a shallow understanding, this book prioritizes a deep understanding of key concepts, ensuring a solid foundation through insightful explanations and comprehensive elaboration, that might otherwise take years to master.
While brainstorming the title for this book, we also contemplated a few other options that may aptly represent the book's focus.
Probability and Statistics for Data Science and Machine Learning - The Practical We all know that academia is often steeped deep in theory and more abstract. This is pretty much the opposite of how software actually works. This book takes a practical approach to ensure that readers don't get lost in the often overly theoretical and abstract content. Instead, it provides practical explanations and real-world examples that you can directly apply. We've structured the content in a rhythmic flow, beginning with the fundamentals of probability and statistics, advancing to more complex concepts, and then progressively applying these principles in data analysis and the construction of machine learning models such as Linear and Logistic regression. This way, you'll not only build a solid foundation of the subjects but also gain the practical skills to effectively apply them in the context of data science and machine learning.
Intuiting Probability and Statistics for Data Science and Machine In this book, our approach prioritizes intuition to deepen the learner’s comprehension. Before delving into the intricacies of math, we emphasize fostering an intuitive foundation. Instead of immediately detailing "How something is done," we methodically build understanding by addressing "Why it's necessary". This approach aims to cultivate a holistic perspective and a deeper revelation into the subject, ensuring that learners not only acquire skills but also grasp the underlying principles and motivations behind them.
Modern Insights into Probability and Statistics for Data Science and Machine In this book, we've embraced a forward-thinking perspective that aligns with today's technology landscape. We've incorporated case studies from recent market trends and have seamlessly blended theory with modern tools and technologies, making the knowledge both timeless and timely. We recognize the value of modern presentation techniques to enhance comprehension and make complex topics easily digestible. In this book, we followed a contemporary style, and employed a visually engaging approach with ample illustrative pictures and diagrams. Additionally, the subject is presented in a relatable manner by using clear and accessible language, removing unnecessary jargon and complexities, and creating a sense of familiarity. We achieved this through clear explanations and examples that are thoughtfully crafted to resonate with readers from diverse backgrounds.
Probability and Statistics for Data Science and Machine Learning explained earnestly (While not a conventional book title, it captures our sentiments aptly): This book is a result of our sheer passion, our commitment to delivering the utmost quality is ingrained in the very essence of this book. The energy and enthusiasm that you’ll experience in the first chapter are sustained throughout, echoing our unwavering dedication to your learning journey.
"Drawing from historical statistics, there is a high probability that this book will prove to be a valuable resource in both your academic and professional pursuits".
While brainstorming the title for this book, we also contemplated a few other options that may aptly represent the book's focus.
Probability and Statistics for Data Science and Machine Learning - The Practical We all know that academia is often steeped deep in theory and more abstract. This is pretty much the opposite of how software actually works. This book takes a practical approach to ensure that readers don't get lost in the often overly theoretical and abstract content. Instead, it provides practical explanations and real-world examples that you can directly apply. We've structured the content in a rhythmic flow, beginning with the fundamentals of probability and statistics, advancing to more complex concepts, and then progressively applying these principles in data analysis and the construction of machine learning models such as Linear and Logistic regression. This way, you'll not only build a solid foundation of the subjects but also gain the practical skills to effectively apply them in the context of data science and machine learning.
Intuiting Probability and Statistics for Data Science and Machine In this book, our approach prioritizes intuition to deepen the learner’s comprehension. Before delving into the intricacies of math, we emphasize fostering an intuitive foundation. Instead of immediately detailing "How something is done," we methodically build understanding by addressing "Why it's necessary". This approach aims to cultivate a holistic perspective and a deeper revelation into the subject, ensuring that learners not only acquire skills but also grasp the underlying principles and motivations behind them.
Modern Insights into Probability and Statistics for Data Science and Machine In this book, we've embraced a forward-thinking perspective that aligns with today's technology landscape. We've incorporated case studies from recent market trends and have seamlessly blended theory with modern tools and technologies, making the knowledge both timeless and timely. We recognize the value of modern presentation techniques to enhance comprehension and make complex topics easily digestible. In this book, we followed a contemporary style, and employed a visually engaging approach with ample illustrative pictures and diagrams. Additionally, the subject is presented in a relatable manner by using clear and accessible language, removing unnecessary jargon and complexities, and creating a sense of familiarity. We achieved this through clear explanations and examples that are thoughtfully crafted to resonate with readers from diverse backgrounds.
Probability and Statistics for Data Science and Machine Learning explained earnestly (While not a conventional book title, it captures our sentiments aptly): This book is a result of our sheer passion, our commitment to delivering the utmost quality is ingrained in the very essence of this book. The energy and enthusiasm that you’ll experience in the first chapter are sustained throughout, echoing our unwavering dedication to your learning journey.
"Drawing from historical statistics, there is a high probability that this book will prove to be a valuable resource in both your academic and professional pursuits".
351 pages, Kindle Edition
Published October 29, 2023
26 people are currently reading
6 people want to read
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Displaying 1 - 2 of 2 reviews
March 31, 2025
Good Attempt
List of topics covered are very relevant. Some concepts are nicely explained. More attention should have been given to accuracy.
List of topics covered are very relevant. Some concepts are nicely explained. More attention should have been given to accuracy.
April 11, 2025
Pretty Good
Not a bad discussion of probability. Can be used by the casual user of statistics to gain rudimentary understanding. Also a resource for students studying stats.
Not a bad discussion of probability. Can be used by the casual user of statistics to gain rudimentary understanding. Also a resource for students studying stats.
Displaying 1 - 2 of 2 reviews