Download the fantastic book titled Machine Learning Pocket Reference written by Matt Harrison, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Machine Learning Pocket Reference", which was released on 27 August 2019. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.
Summary of Machine Learning Pocket Reference by Matt Harrison PDF
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Detail About Machine Learning Pocket Reference PDF
- Author : Matt Harrison
- Publisher : "O'Reilly Media, Inc."
- Genre : Computers
- Total Pages : 320 pages
- ISBN : 149204749X
- Release Date : 27 August 2019
- PDF File Size : 41,8 Mb
- Language : English
- Rating : 4/5 from 21 reviews
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