 Название: Easily: Practical Machine Learning Algorithms with Python
Автор: Darrin Thomas
Издательство: SuJinSoLa
Год: 2020
Страниц: 169
Язык: английский
Формат: pdf, epub
Размер: 11.7 MB
Python is a highly popular language used in programming that has ca large influence in the field of data science. In this text, Darrin Thomas presents various ways to use Python for model development in the context of machine learning. Being able to comprehend the use of Python for data science is a valuable skill for anyone who must deal with data on a regular basis.
One assumption of this text is that the readers is already familiar with Python and that they are able to comprehend and interpret the code in the examples. Of course, as the author of this text, I make no claim to being the ultimate authority on the use of Python and the details of using this language. However, having said this, I do know how to get things done when using this language and want to share what I know to help others.
In addition, it also assumed that the reader is already familiar with the realm of data science and machine learning. As such, there will not be a detailed description of the data science pipeline, classification, regression, data mugging, etc. This book is focused specifically on using the algorithms and not teaching the basics of data science.
Therefore, the target audience for this book are individuals with some background in both coding, statistics, and basic concepts of data science who are looking to extend their skill into using Python for statistical analysis to deal with problems commonly associated with data science.
Contents:
Chapter 1 Decision Trees-Classification
Chapter 2 Decision Trees-Numeric Prediction
Chapter 3 Random Forest-Classification
Chapter 4 Random Forest-Numeric Prediction
Chapter 5 K Nearest Neighbor-Classification & Numeric Prediction
Chapter 6 Support Vector Machines-Classification
Chapter 7 Support Vector Machines-Regression
Chapter 8 Artificial Neural Networks
Chapter 9 K-Means
Chapter 10 Assessing Classification Model Performance
Chapter 11 Assessing Regression Model Performance
Chapter 12 Model Improvement Strategies
Chapter 13 Combining Algorithms
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Easily: Practical Machine Learning Algorithms with Python 
