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Building Machine Learning Systems with Python
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Thisisatutorial-drivenandpractical,butwell-groundedbookshowcasinggoodMachineLearningpractices.Therewillbeanemphasisonusingexistingtechnologiesinsteadofshowinghowtowriteyourownimplementationsofalgorithms.Thisbookisascenario-based,example-driventutorial.BytheendofthebookyouwillhavelearntcriticalaspectsofMachineLearningPythonprojectsandexperiencedthepowerofML-basedsystemsbyactuallyworkingonthem.ThisbookprimarilytargetsPythondeveloperswhowanttolearnaboutandbuildMachineLearningintotheirprojects,orwhowanttoprovideMachineLearningsupporttotheirexistingprojects,andseethemgetimplementedeffectively.Computerscienceresearchers,datascientists,ArtificialIntelligenceprogrammers,andstatisticalprogrammerswouldequallygainfromthisbookandwouldlearnabouteffectiveimplementationthroughlotsofthepracticalexamplesdiscussed.ReadersneednopriorexperiencewithMachineLearningorstatisticalprocessing.Pythondevelopmentexperienceisassumed.
目錄(91章)
倒序
- 封面
- 版權信息
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Preface
- Chapter 1. Getting Started with Python Machine Learning
- Machine learning and Python – the dream team
- What the book will teach you (and what it will not)
- What to do when you are stuck
- Getting started
- Our first (tiny) machine learning application
- Summary
- Chapter 2. Learning How to Classify with Real-world Examples
- The Iris dataset
- Building more complex classifiers
- A more complex dataset and a more complex classifier
- Binary and multiclass classification
- Summary
- Chapter 3. Clustering – Finding Related Posts
- Measuring the relatedness of posts
- Preprocessing – similarity measured as similar number of common words
- Clustering
- Solving our initial challenge
- Tweaking the parameters
- Summary
- Chapter 4. Topic Modeling
- Latent Dirichlet allocation (LDA)
- Comparing similarity in topic space
- Choosing the number of topics
- Summary
- Chapter 5. Classification – Detecting Poor Answers
- Sketching our roadmap
- Learning to classify classy answers
- Fetching the data
- Creating our first classifier
- Deciding how to improve
- Using logistic regression
- Looking behind accuracy – precision and recall
- Slimming the classifier
- Ship it!
- Summary
- Chapter 6. Classification II – Sentiment Analysis
- Sketching our roadmap
- Fetching the Twitter data
- Introducing the Naive Bayes classifier
- Creating our first classifier and tuning it
- Cleaning tweets
- Taking the word types into account
- Summary
- Chapter 7. Regression – Recommendations
- Predicting house prices with regression
- Penalized regression
- P greater than N scenarios
- Summary
- Chapter 8. Regression – Recommendations Improved
- Improved recommendations
- Basket analysis
- Summary
- Chapter 9. Classification III – Music Genre Classification
- Sketching our roadmap
- Fetching the music data
- Looking at music
- Using FFT to build our first classifier
- Improving classification performance with Mel Frequency Cepstral Coefficients
- Summary
- Chapter 10. Computer Vision – Pattern Recognition
- Introducing image processing
- Loading and displaying images
- Classifying a harder dataset
- Local feature representations
- Summary
- Chapter 11. Dimensionality Reduction
- Sketching our roadmap
- Selecting features
- Other feature selection methods
- Feature extraction
- Multidimensional scaling (MDS)
- Summary
- Chapter 12. Big(ger) Data
- Learning about big data
- Using jug to break up your pipeline into tasks
- Using Amazon Web Services (AWS)
- Summary
- Appendix A. Where to Learn More about Machine Learning
- Online courses
- Books
- What was left out
- Summary
- Index 更新時間：2021-08-13 16:36:01
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