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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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Processing

Concepts

Data Science

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (16) Chapters

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

A Gentle Introduction to Machine Learning

In the last few years, machine learning has become one of the most important and prolific IT and artificial intelligence branches. It's not surprising that its applications are becoming more widespread day by day in every business sector, always with new and more powerful tools and results. Open source, production-ready frameworks, together with hundreds of papers published every month, are contributing to one of the most pervasive democratization processes in IT history. But why is machine learning so important and valuable?

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Machine Learning Algorithms

Published in: Jul 2017

Publisher: Packt

ISBN-13: 9781785889622

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