Practical Data Science with R
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language...
Mastering Machine Learning With scikit-learn
Apply effective learning algorithms to real-world problems using scikit-learn
About This Book
Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering
Acquaint yourself with popular machine learning algorithms, including...
Machine Learning in Action
After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on...
Data Science from Scratch: First Principles with Python
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 book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from...
Predictive Analytics: Microsoft Excel
Predictive Analytics: Microsoft® Excel
Excel predictive analytics for serious data crunchers!
The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to help your...
Beginning R: An Introduction to Statistical Programming
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization,...
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