Dimensionality reduction can be roughly grouped into feature selection and feature projection methods. We have already employed some kind of feature selection in almost every chapter so far when we invented, analyzed, and then probably dropped some features. In this chapter, we will present some ways that use statistical methods—namely correlation and mutual information—to be able to do so in vast feature spaces. Feature projection tries to transform the original feature space into a lower-dimensional feature space. This is especially useful when we cannot get rid of features using selection methods, but we still have too many features for our learner. We will demonstrate this using principal component analysis (PCA), linear discriminant analysis (LDA), and multidimensional scaling (MDS).
Building Machine Learning Systems with Python - Third Edition
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Building Machine Learning Systems with Python - Third Edition
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Overview of this book
Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).
With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.
By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.
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Current Title:
Building Machine Learning Systems with Python - Third Edition
Table of Contents (17 chapters)
Preface
Free Chapter
Getting Started with Python Machine Learning
Classifying with Real-World Examples
Regression
Classification I – Detecting Poor Answers
Dimensionality Reduction
Clustering – Finding Related Posts
Recommendations
Artificial Neural Networks and Deep Learning
Classification II – Sentiment Analysis
Topic Modeling
Classification III – Music Genre Classification
Computer Vision
Reinforcement Learning
Bigger Data
Where to Learn More About Machine Learning
Other Books You May Enjoy
Customer Reviews