CANCEL

Subscription

0

Cart

You have no products in your basket yet

Save more on your purchases!
Savings automatically calculated. No voucher code required

Account

eBook

$27.98
Print

$48.99
Subscription

Free Trial

Renews at $19.99p/m
Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

View table of contents
Preview Book

- 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

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.

- • 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

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Jul 24, 2017

Length
254 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781788299879

Vendor :

Google

Category :

Languages :

Concepts :

Tools :

Total
$
152.96
175.97
23.01 saved

$27.98
~~$39.99~~

$24.99
~~$35.99~~

$99.99

=

Total
$
152.96
175.97
23.01 saved

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. The Fundamentals of Machine Learning

2. Simple Linear Regression

3. Classification and Regression with k-Nearest Neighbors

4. Feature Extraction

5. From Simple Linear Regression to Multiple Linear Regression

6. From Linear Regression to Logistic Regression

7. Naive Bayes

8. Nonlinear Classification and Regression with Decision Trees

9. From Decision Trees to Random Forests and Other Ensemble Methods

10. The Perceptron

11. From the Perceptron to Support Vector Machines

12. From the Perceptron to Artificial Neural Networks

13. K-means

14. Dimensionality Reduction with Principal Component Analysis

Index

How do I buy and download an eBook?

How can I make a purchase on your website?

Where can I access support around an eBook?

What eBook formats do Packt support?

What are the benefits of eBooks?

What is an eBook?