One of the major parts of any machine learning system is the data processing pipeline. Before data is fed into the machine learning algorithm for training, we need to process it in different ways to make it suitable for that algorithm. Having a robust data processing pipeline goes a long way in building an accurate and scalable machine learning system. There are a lot of basic functionalities available, and data processing pipelines usually consist of a combination of these. Instead of calling these functions in a nested or loopy way, it's better to use the functional programming paradigm to build the combination. Let's take a look at how to combine these functions to form a reusable function composition. In this recipe, we will create three basic functions and look at how to compose a pipeline.
Python Machine Learning Cookbook
By :
Python Machine Learning Cookbook
By:
Overview of this book
Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Constructing a Classifier
Predictive Modeling
Clustering with Unsupervised Learning
Building Recommendation Engines
Analyzing Text Data
Speech Recognition
Dissecting Time Series and Sequential Data
Image Content Analysis
Biometric Face Recognition
Deep Neural Networks
Visualizing Data
Index
Customer Reviews