Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

An example of an LSTM network with Keras

Even if we haven't analyzed in detail the internal dynamics of LSTM cells, we want to present a simple example of a time-series forecast using this kind of model. For this task, we have chosen a dataset of average Earth temperature anomalies (collected every month) provided by the Global Component of Climate at a Glance (GCAG) and available through DataHub (https://datahub.io).

It is possible to download the CSV files directly from https://datahub.io/core/global-temp; however, I suggest installing the Python package through the pip -U install datapackage command and using the API (as shown in the example) to get all the available datasets.

The first step is downloading and preparing the dataset:

from datapackage import Package

package = Package('https://datahub.io/core/global-temp/datapackage.json')

for resource in package...