Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Classifying face images with SVM

Finally, it is time to build an SVM-based face image classifier using everything you just learned. We will do so in parts, exploring the image dataset.

Exploring the face image dataset

We will use the Labeled Faces in the Wild (LFW) people dataset ( from scikit-learn. It consists of more than 13,000 curated face images of more than 5,000 famous people. Each class has various numbers of image samples.

First, we load the face image data as follows:

>>> from sklearn.datasets import fetch_lfw_people
Downloading LFW metadata:c
Downloading LFW metadata:
Downloading LFW metadata:
Downloading LFW data (~200MB):
>>> face_data = fetch_lfw_people(min_faces_per_person...