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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Toc

Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Parallelism of Statistics and Machine Learning 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The mode function was not implemented in the numpy package.". Any command-line input or output is written as follows:

>>> import numpy as np 
>>> from scipy import stats
>>> data = np.array([4,5,1,2,7,2,6,9,3])
# Calculate Mean
>>> dt_mean = np.mean(data) ;
print ("Mean :",round(dt_mean,2))

New terms and important words are shown in bold.

Warnings or important notes appear like this.
Tips and tricks appear like this.
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