Tull Shaun. Machine Learning: Creating an End to End Solution For Absolute Beginners

Coolbullet Publishing, 2018. — 61 p. — ASIN B07JB516BF.This book will describe step by step, how to take a dataset and create a machine learning model and deploy this to a web application.
Do you want to start building Machine Learning Models without having to wade through lots of theoretical equation dense, lengthy textbooks? Then read this book.
This book is a practical text, designed to get you up and running, and developing machine learning models. This book is accompanied with a dataset and code, so you can work through the examples and create your first Machine Learning Models in no time at all.
Machine Learning Creating an End to End Solution For Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations each step of the way. What I will reiterate, this book is a practical text, so to get the most out of this book, I strongly recommend using the Python code, included, which will allow you to build your own Machine Learning Models. This way even if you do not have much programming experience, you can follow along and start to understand what each part of the code is doing.
In this step-by-step guide you will learn:
What tools and machine learning libraries you need.
How to load datasets.
Data Pre-processing techniques, including feature selection, dealing with missing data and one-hot encoding.
How to build three different Machine Learning Models.
Hyperparameter Tuning, including k-fold Cross Validation and GridsearchCV.
How to Compare Machine Learning Models.
Overfitting / Underfitting and Bias/Variance to improve your machine learning model.
Synthetic Minority Over-sampling Technique.
Model Persistence.
How to deploy your Machine Learning Model to a Web Application.