Approaching (Almost) Any Machine Learning Problem

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 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, create an issue in GitHub repository: https://github.com/abhishekkrthakur/approachingalmost And Subscribe to my youtube channel: https://bit.ly/abhitubesub the book is 3+ years old now and there is currently no plan to update it. Its better to just get free PDF from github 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 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 Subscribe to my youtube Setting up your working environment ..................................................... 5 Supervised vs unsupervised learning ....................................................... 7 Cross-validation ................................................................................... 14 Evaluation metrics ................................................................................ 30 Arranging machine learning projects ................................................... 73 Approaching categorical variables ........................................................ 85 Feature engineering ........................................................................... 142 Feature selection ................................................................................ 155 Hyperparameter optimization ............................................................. 167 Approaching image classification & segmentation .............................. 185 Approaching text classification/regression .......................................... 225 Approaching ensembling and stacking ............................................... 272 Approaching reproducible code & model serving ................................ 283