Building Machine Learning Systems with Python : Bring the power of scikit-learn, keras, tensorflow and much more, 3/e (Paperback)

Luis Pedro Coelho, Wilhelm Richert, Matthieu Brucher



Get more from your data through creating practical machine learning systems with Python

Key Features

  • Build your own Python-based machine learning systems tailored to solve any problem
  • Gain the best use of tools and build your own systems to carry out tasks such as classification, sentiment analysis, reinforcement learning, GAN, autoencoders, neural netoworks any many other areas
  • Includes practical examples to learn how to build systems that can be applied to text, images to solve real world problems

Book Description

Machine learning allow models or systems to learn without being explicitly programmed. Python is one of the preferable language which is used to develop machine learning applications banking on its extensive library support. The book Building Machine Learning Systems with Python, Third Edition will address the trending domains by covering the most used data-sets to build practical machine learning systems.

Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on data-sets, using modeling, creating recommendation systems. With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

By the end of this book you will be able to build machine learning systems doing tasks such as classification, sentiment analysis, reinforcement learning, GAN, autoencoders and many more

What you will learn

  • Build a classification system that can be applied to text, images, or sounds
  • Employ Amazon Web Services to run analysis on the cloud
  • Solve problems related to regression using Tensorflow
  • Explore the steps to add collaborative filtering using Tensorflow
  • Understand different ways to apply DNN (Deep Neural Networks) on structured data

Who This Book Is For

This book is intended for Python developers who wants to learn how to build machine leanring systems with growing complexties. We will be using Python's machine learning capabilities to develop effective solutions at work.