English | Apr. 6, 2018 | ISBN: 1491989386 | 366 Pages | EPUB
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.
Youll find recipes for:
Vectors, matrices, and arrays
Handling numerical and categorical data, text, images, and dates and times
Dimensionality reduction using feature extraction or feature selection
Model evaluation and selection
Linear and logical regression, trees and forests, and k-nearest neighbors
Support vector machines (SVM), naΓ―ve Bayes, clustering, and neural networks
Saving and loading trained models
发帖
雷达卡
加好友,备注cda
京公网安备 11010802022788号
论坛法律顾问：王进律师
知识产权保护声明
免责及隐私声明
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
