We will build a system using real data that is very noisy. This chapter is not for the fainthearted, as we will not arrive at the golden solution for a classifier that achieves 100 percent accuracy. This is because even humans often disagree whether an answer was good or not (just look at some of the comments on the stackoverflow.com website). Quite the contrary, we will find out that some problems like this one are so hard that we have to adjust our initial goals on the way. But on that way, we will start with the nearest neighbor approach, find out why it is not very good for the task, switch over to logistic regression, and arrive at a solution that will achieve a good prediction quality but on a smaller part of the answers. Finally, we will spend some time on how to extract the winner to deploy it on the target system.
Building Machine Learning Systems with Python
Building Machine Learning Systems with Python
Overview of this book
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
Related Content you might be interested in
Current Title:
Building Machine Learning Systems with Python
No titles found
Table of Contents (20 chapters)
Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with Python Machine Learning
Learning How to Classify with Real-world Examples
Clustering – Finding Related Posts
Topic Modeling
Classification – Detecting Poor Answers
Classification II – Sentiment Analysis
Regression – Recommendations
Regression – Recommendations Improved
Classification III – Music Genre Classification
Computer Vision – Pattern Recognition
Dimensionality Reduction
Big(ger) Data
Where to Learn More about Machine Learning
Index
Customer Reviews