| Descript |
300 pages : illustrations, charts ; 23 cm |
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text rdacontent |
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unmediated rdamedia |
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volume rdacarrier |
| Contents |
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 |
| Summary |
Abhishek Thakur is a data scientist and world's first quadruple Grand Master on Kaggle. In this book, he provides approaches to different kinds of machine learning problems. This book is very different from traditional books. It does not explain the algorithms but is more oriented towards when should you use what and how to improve on results of your machine learning models. The book covers categorical variables, feature engineering, feature selection, hyperparameter optimization, image problems, text problems and deploying machine learning/deep learning models. If you are looking for pure basics, this book is not for you. This 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. -- back cover |
| Subject |
Machine learning
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| Call # |
006.31 T364A 2020 |
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