Articles liés à Approaching (Almost) Any Machine Learning Problem

Approaching (Almost) Any Machine Learning Problem - Couverture souple

 
9789390274437: Approaching (Almost) Any Machine Learning Problem

L'édition de cet ISBN n'est malheureusement plus disponible.

Synopsis

"Please note that the official seller in India is Pothi. Other sellers are selling printouts/photocopies for cheaper prices"...
This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book.
Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, create an issue in GitHub And Subscribe to my youtube

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Autres éditions populaires du même titre

9788269211504: Approaching (Almost) Any Machine Learning Problem

Edition présentée

ISBN 10 :  8269211508 ISBN 13 :  9788269211504
Editeur : Abhishek Thakur, 2020
Couverture souple