Designing Machine Learning Systems with Python
O ovoj e-knjizi
This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.
What You Will LearnGain an understanding of the machine learning design processOptimize the error function of your machine learning systemUnderstand the common programming patterns used in machine learningDiscover optimizing techniques that will help you get the most from your dataFind out how to design models uniquely suited to your taskIn DetailMachine 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.
Style and approachThis easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective.
O autoru
David Julian is currently working on a machine learning project with Urban Ecological Systems Ltd and Blue Smart Farms (http://www.bluesmartfarms.com.au) to detect and predict insect infestation in greenhouse crops. He is currently collecting a labeled training set that includes images and environmental data (temperature, humidity, soil moisture, and pH), linking this data to observations of infestation (the target variable), and using it to train neural net models. The aim is to create a model that will reduce the need for direct observation, be able to anticipate insect outbreaks, and subsequently control conditions. There is a brief outline of the project at http://davejulian.net/projects/ues. David also works as a data analyst, I.T. consultant, and trainer.
