Email the Author
You can use this page to email Hisham El-Amir about Machine Learning Pipeline.
About the Book
By reading this book you will learn how to build a machine learning pipeline for a real-life projects, whatever stopped you before from mastering machine learning with python you can easily overcome it with this book, because of easy step-by-step, and example-oriented approach that will help you apply the most straightforward and effective tools to both demonstrative and real-world problems and datasets.
Note: This book is for free and and will always be, so get your copy and we will be glade if you supported us by either with your feedback or some donation.
This book will cover the following:
Part one: Introduction
- an introduction to what is data science tools and how to setup it.
- an introduction to data science pipelines and define it and how to scale it.
- an introduction to machine learning pipelines and how learning is done.
- building a small project to make sure that you are now understand the meaning of pipelines.
Part two: Data
- defining data, types of data and levels of data, because it will help us to understand the data.
- understand and cleaning data process, since it's a very important step in the pipeline
- resampling data to create train-set and test-set, and splitting techniques.
- feature engineering and selection, and that's because not all time the needed variable is visible to us.
Part three: supervised leaning
an introduction to machine learning algorithms, how it works, and it's evaluation. And this part will cover the following algorithms:
- Linear Regression.
- Logistic Regression.
- Decision Trees.
- Support Vector Machines.
About the Editor
Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.