Author: | Gavin Hackeling | ISBN: | 9781788298490 |
Publisher: | Packt Publishing | Publication: | September 11, 2017 |
Imprint: | Packt Publishing | Language: | English |
Author: | Gavin Hackeling |
ISBN: | 9781788298490 |
Publisher: | Packt Publishing |
Publication: | September 11, 2017 |
Imprint: | Packt Publishing |
Language: | English |
Use scikit-learn to solve real-world problems with machine learning
This book is for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them. This book is for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required.
This book examines machine learning models including k-nearest neighbors, logistic regression, naive Bayes, random forests, and support vector machines. You will work through document classification, image recognition, and other example problems.
This book reviews fundamental machine learning concepts such as the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve performance.
Through the book's examples you will become familiar with scikit-learn's API, and learn to use it to solve even the complex data problems with ease.
Use scikit-learn to solve real-world problems with machine learning
This book is for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them. This book is for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required.
This book examines machine learning models including k-nearest neighbors, logistic regression, naive Bayes, random forests, and support vector machines. You will work through document classification, image recognition, and other example problems.
This book reviews fundamental machine learning concepts such as the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve performance.
Through the book's examples you will become familiar with scikit-learn's API, and learn to use it to solve even the complex data problems with ease.