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Introduction to Statistical and Machine Learning Methods for Data Science

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Introduction to Statistical and Machine Learning Methods for Data Science

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Statistical and Machine Learning Approaches for Network Analysis

partition biological networks * An introduction to complex networks measures, statistical properties, and models * Modeling for evolving biological;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Introduction to Statistical Machine Learning

, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine;

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Machine Learning and Data Science

techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised;

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Machine Learning Research Progress

automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also;

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Mathematical Problems in Data Science

, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions;

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The Mathematics of Data

science, as well as many other areas. This book gives an introduction to the mathematical methods that form the foundations of machine learning;

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Practical Statistics for Data Scientists

belongs to Statistical machine learning methods that learn from data Unsupervised learning methods for extracting meaning from unlabeled data;

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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted;

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Statistical Reinforcement Learning

is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the;

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Statistical Reinforcement Learning

is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the;

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Introduction to Data Science

experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis;

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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Statistics and Machine Learning Methods for EHR Data

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;

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Statistical Learning for Big Dependent Data

Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting;

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Essential Statistics for Non-STEM Data Analysts

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data;

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Productive and Efficient Data Science with Python

full spectrum of the Python data science ecosystem to increase productivity. You will: * Write fast and efficient code for data science;

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Productive and Efficient Data Science with Python

how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a;

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Gene Expression Data Analysis

for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology;

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Statistical Learning for Biomedical Data

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as;

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Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are;

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An Introduction to Statistical Learning

popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics;

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An Introduction to Statistical Learning

popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics;

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Big Data in Omics and Imaging, Two Volume Set

FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data;

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Statistics for Data Scientists

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science;

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