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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785889622
Length 360 pages
Edition 1st 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 (16) 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. Linear Regression 5. Logistic Regression 6. Naive Bayes 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Hierarchical Clustering 11. Introduction to Recommendation Systems 12. Introduction to Natural Language Processing 13. Topic Modeling and Sentiment Analysis in NLP 14. A Brief Introduction to Deep Learning and TensorFlow 15. Creating a Machine Learning Architecture

scikit-learn tools for machine learning architectures

Now we're going to present two very important scikit-learn classes that can help the machine learning engineer to create complex processing structures including all the steps needed to generate the desired outcomes from the raw datasets.

Pipelines

scikit-learn provides a flexible mechanism for creating pipelines made up of subsequent processing steps. This is possible thanks to a standard interface implemented by the majority of classes therefore most of the components (both data processors/transformers and classifiers/clustering tools) can be exchanged seamlessly. The class Pipeline accepts a single parameter steps, which is a list of tuples in the form (name of the...

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