Mastering Machine Learning with scikit-learn, 2/e (Paperback)
- 出版商: Packt Publishing
- 出版日期: 2017-07-27
- 售價: $1,460
- 貴賓價: 9.5 折 $1,387
- 語言: 英文
- 頁數: 254
- 裝訂: Paperback
- ISBN: 1788299876
- ISBN-13: 9781788299879
Machine Learning 機器學習
scikit-learn 機器學習, 2/e (Mastering Machine Learning with scikit-learn, 2/e) (簡中版)
scikit-learn 新手的晉級：實作各種機器學習解決方案 (Mastering Machine Learning with scikit-learn, 2/e) (繁中版)
- 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.
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 im4:22 PM 8/2/2017ages using logistic regression and support