Approaching (Almost) Any Machine Learning Problem

ĐẢM BẢO " KHÔNG LỖI , KHÔNG ĐEN HÌNH ,KHÔNG ƯNG HOÀN TRẢ TIỀN "

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Approaching (Almost) Any Machine Learning Problem : 300 pages

Author : Abhishek Thakur

Language : English

1) Book Description

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 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, please create an issue on github repo: https://github.com/abhishekkrthakur/approachingalmost

2 ) About the Author

Abhishek Thakur is a data scientist and world's first 4x grandmaster on Kaggle. His passion lies in solving difficult world problems through data science. Abhishek did his Bachelors in Electronics Engineering from India and moved to Germany for pursuing MSc from University of Bonn, Germany with a focus on image processing and computer vision. He dropped out of PhD in 2015 and since then has been working in industries.proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.