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.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Approaching (Almost) Any Machine Learning Problem for free.
- Title
- Approaching (Almost) Any Machine Learning Problem
- Author(s)
- Abhishek Thakur
- Published
- 2020-06-30
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 300
- Language
- English
- ISBN-10
- 8269211508
- ISBN-13
- 9788269211504
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
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
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