Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
1
Thinking in Machine Learning
Index

What is data?


Data can be stored on a hard drive, streamed through a network, or captured live through sensors such as video cameras and microphones. If we are sampling from physical phenomena, such as a video or sound recording, the space is continuous and effectively infinite. Once this space is sampled, that is digitalized, a finite subset of this space has been created and at least some minimal structure has been imposed on it. The data is on a hard drive, encoded in bits, given some attributes such as a name, creation date, and so on. Beyond this, if the data is to be made use of in an application, we need to ask, "how is the data organized and what kinds of queries does it efficiently support?"

When faced with an unseen dataset, the first phase is exploration. Data exploration involves examining the components and structure of data. How many samples does it contain, and how many dimensions are in each sample? What are the data types of each dimension? We should also get a feel for the...