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Feature Engineering For Machine Learning: Principles And Techniques For Data Scientists
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Feature Engineering Is A Crucial Step In The Machinelearning Pipeline, Yet This Topic Is Rarely Examined On Its Own. With This Practical Book, Youll Learn Techniques For Extracting And Transforming Featuresthe Numeric Representations Of Raw Datainto Formats For Machinelearning Models. Each Chapter Guides You Through A Single Data Problem, Such As How To Represent Text Or Image Data. Together, These Examples Illustrate The Main Principles Of Feature Engineering.Rather Than Simply Teach These Principles, Authors Alice Zheng And Amanda Casari Focus On Practical Application With Exercises Throughout The Book. The Closing Chapter Brings Everything Together By Tackling A Realworld, Structured Dataset With Several Featureengineering Techniques. Python Packages Including Numpy, Pandas, Scikitlearn, And Matplotlib Are Used In Code Examples.Youll Examine: Feature Engineering For Numeric Data: Filtering, Binning, Scaling, Log Transforms, And Power Transforms Natural Text Techniques: Bagofwords, Ngrams, And Phrase Detection Frequencybased Filtering And Feature Scaling For Eliminating Uninformative Features Encoding Techniques Of Categorical Variables, Including Feature Hashing And Bincounting Modelbased Feature Engineering With Principal Component Analysis The Concept Of Model Stacking, Using Kmeans As A Featurization Technique Image Feature Extraction With Manual And Deeplearning Techniques
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has two hundred fifteen pages. It provides extensive insights into feature engineering techniques for data scientists.
- Q: What is the binding type of this book? A: The binding type is paperback. This makes it lightweight and portable for easy reading.
- Q: What are the dimensions of this book? A: The dimensions are seven point zero one inches in length, zero point four inches in width, and nine point one inches in height. These dimensions make it a standard-sized paperback.
- Q: How do I apply the techniques from this book? A: You can apply the techniques by following the practical exercises and examples provided in each chapter. This book guides you through various data problems, focusing on real-world applications.
- Q: Is this book suitable for beginners in data science? A: Yes, this book is suitable for beginners. It covers fundamental concepts of feature engineering while also providing practical examples for hands-on learning.
- Q: Can I use this book for advanced data engineering techniques? A: Yes, it includes advanced techniques such as model-based feature engineering and image feature extraction. This makes it beneficial for both beginners and advanced users.
- Q: How should I store this book? A: You should store it in a cool, dry place away from direct sunlight. This will help preserve its condition and prevent any damage over time.
- Q: Is this book safe for children? A: Yes, the content is educational and focused on data science concepts. However, adult supervision is recommended for younger readers due to the complexity of the subject matter.
- Q: What if I receive a damaged copy of this book? A: If you receive a damaged copy, you should contact the seller for their return policy. Most sellers provide options for returns or exchanges in such cases.
- Q: Does this book come with any online resources? A: No, this book does not come with dedicated online resources. However, it references commonly used Python packages which you can explore separately.
- Q: What programming language does this book use for examples? A: The book uses Python for code examples. It includes various libraries like numpy, Pandas, Scikit-learn, and Matplotlib.
- Q: Who are the authors of this book? A: The authors are Alice Zheng and Amanda Casari. They are knowledgeable data scientists who focus on practical applications in this book.
- Q: What topics are covered in this book? A: The book covers topics like feature engineering for numeric and text data, encoding techniques, and model stacking. Each topic is explored through practical examples.
- Q: Is there an index or glossary in this book? A: Yes, the book includes an index for easy reference. This helps readers quickly locate topics of interest.
- Q: Can I find exercises in this book? A: Yes, the book includes exercises throughout. These exercises help reinforce the concepts and techniques discussed in each chapter.