Fundamentals of Machine Learning for Life by Ankur Awadhiya (.ePUB)
File Size: 10.5 MB
Fundamentals of Machine Learning for Life Sciences: A Simple, Step-by-Step Approach with Examples in R (Machine Learning for Life Sciences) by Ankur Awadhiya
Requirements: .ePUB reader, 10.5 MB | True EPUB
Overview: Machine Learning touches our lives in quiet and remarkable ways. It helps doctors detect illnesses sooner by recognizing subtle patterns in scans and helping them make sense of medical data with speed, judgment, and care. It helps us care for our fields and forests by tracking changes that unfold over time. It helps us study the weather with a memory far longer than our own, and notices small signs that equipment may soon need attention. And when we find ourselves in unfamiliar places, it helps us translate words, find our bearings, and discover new corners to explore. And yet, even as it becomes part of daily life, its inner workings can still feel distant when you first encounter them. Fundamentals of Machine Learning aims to bring it within reach. This book offers a clear and steady introduction to how machines learn from data. It explains how models begin to understand, decide, improve, and sometimes falter. Ideas build gradually, one upon another, supported by real examples and datasets in R. The focus is insight over jargon, clarity over complexity. As these ideas become familiar, they also hold the promise of supporting the works of scientists, engineers, and students — by opening new pathways of exploration. R is a programming language and software environment primarily designed for statistical computing and data analysis. Developed by Ross Ihaka and Robert Gentleman at the University of Auckland in the mid-1990s, R has grown into one of the most widely used tools in the Data Science and statistical communities. Its evolution has been marked by a strong emphasis on extensibility and flexibility, making it a versatile choice for a wide range of applications, from simple data manipulation to complex statistical modeling. At its core, R provides a comprehensive suite of tools for performing a variety of statistical analyses. Users can perform descriptive statistics, such as calculating means and standard deviations, and conduct inferential statistics, including hypothesis testing and regression analysis. R supports a vast array of statistical techniques, from basic procedures to more advanced methodologies such as generalized linear models, time series analysis, and multivariate analysis. This extensive range of built-in functions enables users to conduct rigorous statistical evaluations and derive insights from their data.
Genre: Non-Fiction > Tech & Devices
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