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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

Visualizing high-dimensional datasets using t-SNE

Before ending this chapter, I want to introduce the reader to a very powerful algorithm called t-Distributed Stochastic Neighbor Embedding (t-SNE), which can be employed to visualize high-dimensional dataset also in 2D plots. In fact, one the hardest problems that every data scientist has to face is to understand the structure of a complex dataset without the support of graphs. This algorithm has been proposed by Van der Maaten and Hinton (in Visualizing High-Dimensional Data Using t-SNE, Van der Maaten L.J.P., Hinton G.E., Journal of Machine Learning Research 9 (Nov), 2008), and can be used to reduce the dimensionality trying to preserve the internal relationships. A complete discussion is beyond the scope of this book (but the reader can check out the aforementioned paper and Mastering Machine Learning Algorithms, Bonaccorso...

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