Python Machine Learning Cookbook : Over 100 Recipes to Progress from Smart Data Analytics to Deep Learning Using Real-World Datasets, 2nd Edition.

With this book, you will learn how to perform various machine learning tasks in different environments. You'll use a wide variety of machine learning algorithms using Python to solve real-world problems. By the end of the book, you will learn to implement most used machine learning algorithms u...

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Bibliographic Details
Online Access: Full text (MCPHS users only)
Main Author: Ciaburro, Giuseppe
Other Authors: Joshi, Prateek
Format: Electronic eBook
Language:English
Published: Birmingham : Packt Publishing Ltd, 2019
Edition:2nd ed.
Subjects:
Local Note:ProQuest Ebook Central

MARC

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245 1 0 |a Python Machine Learning Cookbook :  |b Over 100 Recipes to Progress from Smart Data Analytics to Deep Learning Using Real-World Datasets, 2nd Edition. 
250 |a 2nd ed. 
260 |a Birmingham :  |b Packt Publishing Ltd,  |c 2019. 
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505 0 |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: The Realm of Supervised Learning; Technical requirements; Introduction; Array creation in Python; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Data preprocessing using mean removal; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Data scaling; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Normalization; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Binarization 
505 8 |a Getting readyHow to do it ... ; How it works ... ; There's more ... ; See also; One-hot encoding; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Label encoding; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Building a linear regressor; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Computing regression accuracy; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Achieving model persistence; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Building a ridge regressor 
505 8 |a Getting readyHow to do it ... ; How it works ... ; See also; Building a polynomial regressor; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Estimating housing prices; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Computing the relative importance of features; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Estimating bicycle demand distribution; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 2: Constructing a Classifier; Technical requirements; Introduction 
505 8 |a Building a simple classifierGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Building a logistic regression classifier; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Building a Naive Bayes classifier; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Splitting a dataset for training and testing; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Evaluating accuracy using cross-validation metrics; Getting ready ... ; How to do it ... ; How it works ... ; There's more ... ; See also 
505 8 |a Visualizing a confusion matrixGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Extracting a performance report; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Evaluating cars based on their characteristics; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Extracting validation curves; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Extracting learning curves; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Estimating the income bracket; Getting ready; How to do it ... 
520 |a With this book, you will learn how to perform various machine learning tasks in different environments. You'll use a wide variety of machine learning algorithms using Python to solve real-world problems. By the end of the book, you will learn to implement most used machine learning algorithms using complex datasets and optimized techniques. 
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