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Machine Learning with Python Cookbook : Practical Solutions from Preprocessing to Deep Learning by Chris Albon (2018, Trade Paperback)
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- Buy It NowMachine Learning with Python Cookbook: Practical Solutions from Preprocessing to
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101491989386
ISBN-139781491989388
eBay Product ID (ePID)239614030
Product Key Features
Number of Pages364 Pages
Publication NameMachine Learning with Python Cookbook : Practical Solutions from Preprocessing to Deep Learning
LanguageEnglish
SubjectData Modeling & Design, Data Processing, Databases / Data Mining, Programming Languages / Python
Publication Year2018
TypeTextbook
AuthorChris Albon
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight21.9 Oz
Item Length9.2 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
LCCN2017-278943
IllustratedYes
SynopsisThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you'??re comfortable with Python and its libraries, including pandas and scikit-learn, you'??ll 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. You'??ll 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), na�¯ve Bayes, clustering, and neural networks Saving and loading trained models, This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll 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. You'll 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), na ve Bayes, clustering, and neural networks Saving and loading trained models, This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you' re comfortable with Python and its libraries, including pandas and scikit-learn, you' ll 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. You' ll 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), na� ve Bayes, clustering, and neural networks Saving and loading trained models
LC Classification NumberQ325.5.A4 2018
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