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Mastering Data Mining with Python:Find patterns hidden in your data
最新章節:
Index
ThisbookisfordatascientistswhoarealreadyfamiliarwithsomebasicdataminingtechniquessuchasSQLandmachinelearning,andwhoarecomfortablewithPython.Ifyouarereadytolearnsomemoreadvancedtechniquesindatamininginordertobecomeadataminingexpert,thisisthebookforyou!
目錄(61章)
倒序
- 封面
- 版權頁
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- eBooks discount offers and more
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Expanding Your Data Mining Toolbox
- What is data mining?
- How do we do data mining?
- What are the techniques used in data mining?
- How do we set up our data mining work environment?
- Summary
- Chapter 2. Association Rule Mining
- What are frequent itemsets?
- Towards association rules
- A project – discovering association rules in software project tags
- Summary
- Chapter 3. Entity Matching
- What is entity matching?
- Entity matching project
- Summary
- Chapter 4. Network Analysis
- What is a network?
- Measuring a network
- Representing graph data
- A real project
- Summary
- Chapter 5. Sentiment Analysis in Text
- What is sentiment analysis?
- The basics of sentiment analysis
- Sentiment analysis algorithms
- Sentiment mining application
- Summary
- Chapter 6. Named Entity Recognition in Text
- Why look for named entities?
- Techniques for named entity recognition
- Building and evaluating NER systems
- Named entity recognition project
- Summary
- Chapter 7. Automatic Text Summarization
- What is automatic text summarization?
- Tools for text summarization
- Summary
- Chapter 8. Topic Modeling in Text
- What is topic modeling?
- Latent Dirichlet Allocation
- Gensim for topic modeling
- Gensim LDA for a larger project
- Summary
- Chapter 9. Mining for Data Anomalies
- What are data anomalies?
- Summary
- Index 更新時間:2021-08-20 10:33:37
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