Data science solutions with Python: fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn
Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distribute...
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| Format: | Buch |
| Sprache: | English |
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New York
Apress
2022
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. -- |
| Beschreibung: | Illustrationen, Diagramme 26 cm |
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| 520 | |a Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. -- | ||
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| adam_text | Table of Contents About the Author........ ................ ¡X About the Technical Reviewer............................. xi Acknowledgments......................................................................... xiii Introduction........ ..................... -xv Chapter 1: Exploring Machine Learning........................... 1 Exploring Supervised Methods....................................................................................................... 1 Exploring Nonlinear Models....................................................................................................... 2 Exploring Ensemble Methods....................................................................................................3 Exploring Unsupervised Methods...................................................................................................3 Exploring Cluster Methods........................................................................................................ 3 Exploring Dimension Reduction.................................................................................................4 Exploring Deep Learning................................................................................................................. 4 Conclusion...................................................................................................................................... 5 Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks.............. 7 Big
Data...........................................................................................................................................7 Big Data Features...................................................................................................................... 8 Impact of Big Data on Business and People............. .................................... 8 Better Customer Relationships..................................................................................................8 Refined Product Development...................................................................................................9 Improved Decision-Making........................................................................................................ 9 Big Datawarehousing..................................................................................................................... 9 Big Data ETL....................................................... 9 Big Data Frameworks.................................................................................................................... 10 Apache Spark.......................................................................................................................... 10 V
TABLE OF CONTENTS ML Frameworks................................................................................................................................. 13 Scikit-Learn...................................................................................................................................13 H20................................................................................................................................................ 13 XGBoost......................................................................................................................................... 14 DL Frameworks............................................. 14 Keras............................................................................................................................................. 14 Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H20......... 15 Exploring the Ordinary Least-Squares Method................................................................................15 Scikit-Learn in Action......................................................................................................................... 17 PySpark in Action.........................................................................................................................20 H20 in Action................................................................................................................................ 22
Conclusion..........................................................................................................................................28 Chapter 4: Survival Analysis with PySpark and Lifelines....... ........ 29 Exploring Survival Analysis............................................................................................................... 29 Exploring Cox Proportional Hazards Method..................................................................... 29 Lifeline in Action.................................................................. 30 Exploring the Accelerated Failure Time Method...............................................................................34 PySpark in Action.........................................................................................................................34 Conclusion..........................................................................................................................................37 Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H20.......................39 Exploring the Logistic Regression Method....................................................................................... 39 Scikit-Learn in Action...................................................................................................................41 PySpark in Action.........................................................................................................................48 H20 in
Action.......................................................................................................................................52 Conclusion.......................................................................................................................................... 57 vi
TABLE OF CONTENTS Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H20............................................................................................ 59 Decision Trees............................................................................................................................................ 59 Preprocessing Features............................................................................................................................60 Scikit-Learn in Action..........................................................................................................................61 Gradient Boosting....................................................................................................................................... 66 XGBoost in Action................................................................................................................................ 66 PySpark in Action................................................................................................................................ 69 H20 in Action........................................................................................................................................ 71 Conclusion.................................................................................................................................................. 74 Chapter 7: Neural Networks with Scikit-Learn, Keras, and H20.................... 75 Exploring Deep
Learning...........................................................................................................................75 Multilayer Perceptron Neural Network................................................................................................... 75 Preprocessing Features............................................................................................................................ 76 Scikit-Learn in Action................................................................................................................................ 77 Keras in Action...................................................................................................................................... 82 Deep Belief Networks......................................................................................................................... 87 H20 in Action........................................................................................................................................ 87 Conclusion.................................................................................................................................................. 88 Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H20............. 89 Exploring the К-Means Method............................................................................................................... 89 Scikit-Learn in Action................................................................................................................................ 91 PySpark in
Action................................................................................................................................ 93 H20 in Action........................................................................................................................................ 97 Conclusion........................................................................................................... 99 vii
TABLE OF CONTENTS Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H20 ..................................................... Exploring the Principal Component Method................................................................................101 Scikit-Learn in Action.................................................................................................................. 102 PySpark in Action.........................................................................................................................105 H20 in Action......................................................................................................................... 109 Conclusion...................................................................................................................................110 Chapter 10: Automating the Machine Learning Process with H20................. Exploring Automated Machine Learning..... ..............................................................................111 Preprocessing Features.............................................................................................................. 112 H20 AutoML in Action............................................................................................................ 112 Conclusion...................................................................................................................................116 Index................................................................................................................ viii 111
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| adam_txt |
Table of Contents About the Author. . ¡X About the Technical Reviewer. xi Acknowledgments. xiii Introduction. . -xv Chapter 1: Exploring Machine Learning. 1 Exploring Supervised Methods. 1 Exploring Nonlinear Models. 2 Exploring Ensemble Methods.3 Exploring Unsupervised Methods.3 Exploring Cluster Methods. 3 Exploring Dimension Reduction.4 Exploring Deep Learning. 4 Conclusion. 5 Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks. 7 Big
Data.7 Big Data Features. 8 Impact of Big Data on Business and People. . 8 Better Customer Relationships.8 Refined Product Development.9 Improved Decision-Making. 9 Big Datawarehousing. 9 Big Data ETL. 9 Big Data Frameworks. 10 Apache Spark. 10 V
TABLE OF CONTENTS ML Frameworks. 13 Scikit-Learn.13 H20. 13 XGBoost. 14 DL Frameworks. 14 Keras. 14 Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H20. 15 Exploring the Ordinary Least-Squares Method.15 Scikit-Learn in Action. 17 PySpark in Action.20 H20 in Action. 22
Conclusion.28 Chapter 4: Survival Analysis with PySpark and Lifelines. . 29 Exploring Survival Analysis. 29 Exploring Cox Proportional Hazards Method. 29 Lifeline in Action. 30 Exploring the Accelerated Failure Time Method.34 PySpark in Action.34 Conclusion.37 Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H20.39 Exploring the Logistic Regression Method. 39 Scikit-Learn in Action.41 PySpark in Action.48 H20 in
Action.52 Conclusion. 57 vi
TABLE OF CONTENTS Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H20. 59 Decision Trees. 59 Preprocessing Features.60 Scikit-Learn in Action.61 Gradient Boosting. 66 XGBoost in Action. 66 PySpark in Action. 69 H20 in Action. 71 Conclusion. 74 Chapter 7: Neural Networks with Scikit-Learn, Keras, and H20. 75 Exploring Deep
Learning.75 Multilayer Perceptron Neural Network. 75 Preprocessing Features. 76 Scikit-Learn in Action. 77 Keras in Action. 82 Deep Belief Networks. 87 H20 in Action. 87 Conclusion. 88 Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H20. 89 Exploring the К-Means Method. 89 Scikit-Learn in Action. 91 PySpark in
Action. 93 H20 in Action. 97 Conclusion. 99 vii
TABLE OF CONTENTS Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H20 . Exploring the Principal Component Method.101 Scikit-Learn in Action. 102 PySpark in Action.105 H20 in Action. 109 Conclusion.110 Chapter 10: Automating the Machine Learning Process with H20. Exploring Automated Machine Learning. .111 Preprocessing Features. 112 H20 AutoML in Action. 112 Conclusion.116 Index. viii 111 |
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| spelling | Nokeri, Tshepo Chris Verfasser (DE-588)1232349984 aut Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn Tshepo Chris Nokeri New York Apress 2022 Illustrationen, Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. -- Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Data Science (DE-588)1140936166 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033279638&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nokeri, Tshepo Chris Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast Python Programmiersprache (DE-588)4434275-5 gnd Data Science (DE-588)1140936166 gnd |
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| title | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn |
| title_auth | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn |
| title_exact_search | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn |
| title_exact_search_txtP | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn |
| title_full | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn Tshepo Chris Nokeri |
| title_fullStr | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn Tshepo Chris Nokeri |
| title_full_unstemmed | Data science solutions with Python fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn Tshepo Chris Nokeri |
| title_short | Data science solutions with Python |
| title_sort | data science solutions with python fast and scalable models using keras pyspark mllib h2o xgboost and scikit learn |
| title_sub | fast and scalable models using Keras, PySpark MLlib, H2o, XGBoost, and Scikit-Learn |
| topic | Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast Python Programmiersprache (DE-588)4434275-5 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Machine learning Python (Computer program language) Python Programmiersprache Data Science |
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