An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged …
Shelve An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
A series of Deep Learning breakthroughs have boosted the whole field of machine learning over the last decade. Now that machine learning is thriving, even programmers who know close to nothing about t…
Shelve Hands-On Machine Learning with Scikit-Learn and TensorFlow
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marke…
Shelve The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing i…
In this "important and comprehensive" guide to statistical thinking ( New Yorker ), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems.
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Shelve The Art of Statistics: How to Learn from Data
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make …
Shelve R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Don't simply show your data — tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of…
Shelve Storytelling with Data: A Data Visualization Guide for Business Professionals
Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, w…
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. Th…
Shelve Mathematics for Machine Learning: 1st Edition
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non- mathematical readers will appreciate the intuitive explanations of the…
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
Best Loser Wins is an intimate insight into one of the most prolific high-stake retail traders in the world. Tom Hougaard is the winner of multiple trading competitions and on one occasion traded the …
Shelve Best Loser Wins: Why Normal Thinking Never Wins the Trading Game – written by a high-stake day trader
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and opti…
Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning…
Shelve The StatQuest Illustrated Guide To Machine Learning
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this…
Shelve Data Science from Scratch: First Principles with Python
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic think…
Shelve Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
HarperCollins is proud to present its incredible range of best-loved, essential classics. No man can live a happy life, or even a supportable life, without the study of wisdom
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they ha…
Shelve Pattern Recognition and Machine Learning (Information Science and Statistics)
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'l…
Shelve Fundamentals of Data Engineering: Plan and Build Robust Data Systems
Even bad code can function. But if code isn't clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code…
Shelve Clean Code: A Handbook of Agile Software Craftsmanship
We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made …
Shelve Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
