دانلود کتاب Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

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Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Exploring Machine Learning
Exploring Supervised Methods
Exploring Nonlinear Models
Exploring Ensemble Methods
Exploring Unsupervised Methods
Exploring Cluster Methods
Exploring Dimension Reduction
Exploring Deep Learning
Conclusion
Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks
Big Data
Big Data Features
Impact of Big Data on Business and People
Better Customer Relationships
Refined Product Development
Improved Decision-Making
Big Data Warehousing
Big Data ETL
Big Data Frameworks
Apache Spark
Resilient Distributed Data Sets
Spark Configuration
Spark Frameworks
SparkSQL
Spark Streaming
Spark MLlib
GraphX
ML Frameworks
Scikit-Learn
H2O
XGBoost
DL Frameworks
Keras
Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O
Exploring the Ordinary Least-Squares Method
Scikit-Learn in Action
PySpark in Action
H2O in Action
Conclusion
Chapter 4: Survival Analysis with PySpark and Lifelines
Exploring Survival Analysis
Exploring Cox Proportional Hazards Method
Lifeline in Action
Exploring the Accelerated Failure Time Method
PySpark in Action
Conclusion
Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O
Exploring the Logistic Regression Method
Scikit-Learn in Action
PySpark in Action
H2O in Action
Conclusion
Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O
Decision Trees
Preprocessing Features
Scikit-Learn in Action
Gradient Boosting
XGBoost in Action
PySpark in Action
H2O in Action
Conclusion
Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O
Exploring Deep Learning
Multilayer Perceptron Neural Network
Preprocessing Features
Scikit-Learn in Action
Keras in Action
Deep Belief Networks
H2O in Action
Conclusion
Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H2O
Exploring the K-Means Method
Scikit-Learn in Action
PySpark in Action
H2O in Action
Conclusion
Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H2O
Exploring the Principal Component Method
Scikit-Learn in Action
PySpark in Action
H2O in Action
Conclusion
Chapter 10: Automating the Machine Learning Process with H2O
Exploring Automated Machine Learning
Preprocessing Features
H2O AutoML in Action
Conclusion
Index