Book Image

Building Machine Learning Systems with Python - Third Edition

By : Luis Pedro Coelho, Willi Richert, Matthieu Brucher
Book Image

Building Machine Learning Systems with Python - Third Edition

By: Luis Pedro Coelho, Willi Richert, Matthieu Brucher

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.
Table of Contents (17 chapters)
Free Chapter
1
Getting Started with Python Machine Learning

Looking at music

A very convenient way to get a quick impression of what the songs of diverse genres "look" like is to draw a spectrogram for a set of songs in a genre. A spectrogram is a visual representation of the frequencies that occur in a song. It shows the intensity for the frequencies on the y axis in the specified time intervals on the x axis. In the following spectrogram, that would mean the brighter the color, the stronger the frequency in the particular time window of the song.

Matplotlib provides the convenient specgram() function, which performs most of the under-the-hood calculation and plotting for us:

>>> import scipy.io.wavfile
>>> from matplotlib.pyplot import specgram
>>> sample_rate, X = scipy.io.wavfile.read(wave_filename)
>>> print(sample_rate, X.shape)
22050, (661794,)
>>> specgram(X, Fs=sample_rate, xextent...