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Becoming a Data Head - How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman;

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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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Data Science Algorithms in a Week

help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large;

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Data Science for Marketing Analytics

by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data;

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Big Data and Machine Learning in Quantitative Investment

* Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately;

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Julia for Data Science

learning framework and build a system using Mocha.jl In Detail Julia is a fast and high performing language that's perfectly suited to data;

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The Mathematics of Data

interested in understanding how applied mathematics broadly defined is being used in data science. It will appeal to anyone interested in the;

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Data Science Solutions on Azure

Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools;

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Machine Learning in Java

amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key;

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Machine Learning Algorithms

. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key;

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Data Science Algorithms in a Week

exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for;

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Information-Theoretic Methods in Data Science

reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and;

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Business Data Science

any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The;

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Beginning Data Science in R 4

, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build;

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Hands-On Data Science and Python Machine Learning

the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the;

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Data Science

This book, Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data;

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A Tour of Data Science

, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data;

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A Tour of Data Science

, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data;

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Probability and Statistics for Data Science

logistic regression, and neural networks. * Leads the student to think critically about the how and why of statistics, and to see the big;

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Probability and Statistics for Data Science

logistic regression, and neural networks. * Leads the student to think critically about the how and why of statistics, and to see the big;

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Python Machine Learning

skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science;

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Statistics and Machine Learning Methods for EHR Data

of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book;

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Numerical Computing with Python

level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following;

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Machine Learning Research Progress

that allow computers to learn . At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning;

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Data Science Projects with Python

-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to;

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Python Machine Learning

elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.;

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Practical Statistics for Data Scientists

can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to;

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