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Mastering Machine Learning with scikit-learn
Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn: Apply effective learning algorithms to real-world problems using scikit-learn , Second Edition

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Profile Icon Gavin Hackeling
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
Paperback Jul 2017 254 pages 2nd Edition
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€8.99 €29.99
Paperback
€36.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Gavin Hackeling
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
Paperback Jul 2017 254 pages 2nd Edition
eBook
€8.99 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

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Mastering Machine Learning with scikit-learn

Simple Linear Regression

In this chapter, we will introduce our first model, simple linear regression. Simple linear regression models the relationship between one response variable and one feature of an explanatory variable. We will discuss how to fit our model, and we will work through a toy problem. While simple linear regression is rarely applicable to real-world problems, understanding it is essential to understanding many other models. In subsequent chapters, we will learn about generalizations of simple linear regression and apply them to real-world datasets.

Simple linear regression

In the previous chapter, we learned that training data is used to estimate the parameters of a model in supervised learning problems. Observations of explanatory variables and their corresponding response variables comprise training data. The model can be used to predict the value of the response variable for values of the explanatory variable that have not been previously observed. Recall that the goal in regression problems is to predict the value of a continuous response variable. In this chapter, we will examine simple linear regression, which can be used to model a linear relationship between one response variable and one feature representing an explanatory variable.

Suppose you wish to know the price of a pizza. You might simply look at a menu. This, however, is a machine learning book, so instead we will use simple linear regression to predict the...

Evaluating the model

We have used a learning algorithm to estimate a model's parameters from training data. How can we assess whether our model is a good representation of the real relationship? Let's assume that you have found another page in your pizza journal. We will use this page's entries as a test set to measure the performance of our model. We have added a fourth column; it contains the prices predicted by our model.

Test instance

Diameter in inches

Observed price in dollars

Predicted price in dollars

1

8

11

9.7759

2

9

8.5

10.7522

3

11

15

12.7048

4

16

18

17.5863

5

12

11

13.6811

 

Several measures can be used to assess our model's predictive capability. We will evaluate our pizza price predictor using a measure called R-squared. Also known as the coefficient of determination, R-squared...

Summary

In this chapter, we introduced simple linear regression, which models the relationship between a single explanatory variable and a continuous response variable. We worked through a toy problem to predict the price of a pizza from its diameter. We used the residual sum of squares cost function to assess the fitness of our model, and analytically solved the values of our model's parameter that minimized the cost function. We measured the performance of our model on a test set. Finally, we introduced scikit-learn's estimator API. In the next chapter, we will compare and contrast simple linear regression with another simple, ubiquitous model, k-Nearest Neighbors (KNN).

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Key benefits

  • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
  • Learn how to build and evaluate performance of efficient models using scikit-learn
  • Practical guide to master your basics and learn from real life applications of machine learning

Description

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Who is this book for?

This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required.

What you will learn

  • • Review fundamental concepts such as bias and variance
  • • Extract features from categorical variables, text, and images
  • • Predict the values of continuous variables using linear regression and K Nearest Neighbors
  • • Classify documents and images using logistic regression and support vector machines
  • • Create ensembles of estimators using bagging and boosting techniques
  • • Discover hidden structures in data using K-Means clustering
  • • Evaluate the performance of machine learning systems in common tasks

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 24, 2017
Length: 254 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788299879
Vendor :
Google
Category :
Languages :

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Product Details

Publication date : Jul 24, 2017
Length: 254 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788299879
Vendor :
Google
Category :
Languages :

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Table of Contents

14 Chapters
The Fundamentals of Machine Learning Chevron down icon Chevron up icon
Simple Linear Regression Chevron down icon Chevron up icon
Classification and Regression with k-Nearest Neighbors Chevron down icon Chevron up icon
Feature Extraction Chevron down icon Chevron up icon
From Simple Linear Regression to Multiple Linear Regression Chevron down icon Chevron up icon
From Linear Regression to Logistic Regression Chevron down icon Chevron up icon
Naive Bayes Chevron down icon Chevron up icon
Nonlinear Classification and Regression with Decision Trees Chevron down icon Chevron up icon
From Decision Trees to Random Forests and Other Ensemble Methods Chevron down icon Chevron up icon
The Perceptron Chevron down icon Chevron up icon
From the Perceptron to Support Vector Machines Chevron down icon Chevron up icon
From the Perceptron to Artificial Neural Networks Chevron down icon Chevron up icon
K-means Chevron down icon Chevron up icon
Dimensionality Reduction with Principal Component Analysis Chevron down icon Chevron up icon

Customer reviews

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1 star 0%
Spencer C. Sep 15, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book introduces algorithms with their pros and cons as well as using them on datasets to show how they are invoked from scikit-learn. I really enjoy having a resource near me to quickly get implementation or algorithm details.
Amazon Verified review Amazon
Kindle Customer Mar 23, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very usefulExcellent book for classification , step by step code is given to do practical implementation... nicely explained....good book
Amazon Verified review Amazon
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