Название: Approaching (Almost) Any Machine Learning Problem Автор: Abhishek Thakur
Издательство: Abhishek Thakur
Год: 2020
Страниц: 300
Язык: английский
Формат: pdf (true)
Размер: 11.8 MB
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.
Before we begin with coding, it’s essential to get everything set-up on your machine. Throughout this book, we will be using Ubuntu 18.04 and Python 3.7.6. If you are a Windows user, you can install Ubuntu in multiple ways. On a virtual machine, for example, Virtual Box which is provided by Oracle and is free software. Alongside Windows as a dual boot system. I prefer dual boot as it is native. If you are not an Ubuntu user, you might face problems with some of the bash scripts in this book. To circumvent that you can install Ubuntu in a VM or go for Linux shell on Windows.
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
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