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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms Popular algorithms for data science and machine learning

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789347999
Length 522 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (19) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Regression Algorithms 5. Linear Classification Algorithms 6. Naive Bayes and Discriminant Analysis 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Advanced Clustering 11. Hierarchical Clustering 12. Introducing Recommendation Systems 13. Introducing Natural Language Processing 14. Topic Modeling and Sentiment Analysis in NLP 15. Introducing Neural Networks 16. Advanced Deep Learning Models 17. Creating a Machine Learning Architecture 18. Other Books You May Enjoy

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...
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