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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Relevant research fields

Parallel to AI, another field has continuously gained traction over the past decades. ML is how a computer system develops its intelligence, used by AI to carry out its tasks. Their relation is shown in Figure 1.1:

Figure 1.1 – How AI, ML, DL, and NLP are related

ML is a subset of AI and its intelligence is encompassed by a model trained over several iterations on a large amount of data. With minimal human intervention, the ML algorithm tries to identify patterns from past experiences and develop an efficient model to make predictions. As the ML algorithm is exposed to more data over time, its performance improves.

Interesting fact

The term machine learning was coined in 1959 by Arthur Samuel as the field of study that allows computers to learn without being explicitly programmed.

One way to perform training is to use a special kind of architecture stemming from deep learning (DL). DL algorithms mimic the human brain...

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