"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 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 servingThere 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, please create an issue on github repo: https://github.com/abhishekkrthakur/approachingalmostAnd Subscribe to my youtube channel: https://bit.ly/abhitubesub
Details e-book Approaching (Almost) Any Machine Learning Problem
🗸 Author(s): Abhishek Thakur
🗸 Title: Approaching (Almost) Any Machine Learning Problem
🗸 Rating : 4.5 from 5 stars (621 reviews)
🗸 ISBN-10: 8269211508
🗸 ISBN-13: 9788269211504
🗸 Languange: English
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, PC and Amazon Kindle
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