راهکارهای علم داده با پایتون: مدل‌های سریع و مقیاس‌پذیر با استفاده از keras، pyspark mllib، h2o...، xgboost و 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, 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. **What You Will Learn*** Understand widespread supervised and unsupervised learning, including key dimension reduction techniques * Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning * Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks * Design, build, test, and validate skilled machine models and deep learning models * Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration **Who This Book Is For** Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics Table of Contents 5 About the Author 9 About the Technical Reviewer 10 Acknowledgments 11 Introduction 12 Chapter 1: Exploring Machine Learning 14 Exploring Supervised Methods 14 Exploring Nonlinear Models 15 Exploring Ensemble Methods 16 Exploring Unsupervised Methods 16 Exploring Cluster Methods 16 Exploring Dimension Reduction 17 Exploring Deep Learning 17 Conclusion 18 Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks 19 Big Data 19 Big Data Features 20 Impact of Big Data on Business and People 20 Better Customer Relationships 20 Refined Product Development 21 Improved Decision-Making 21 Big Data Warehousing 21 Big Data ETL 21 Big Data Frameworks 22 Apache Spark 22 Resilient Distributed Data Sets 23 Spark Configuration 23 Spark Frameworks 23 SparkSQL 24 Spark Streaming 24 Spark MLlib 24 GraphX 24 ML Frameworks 25 Scikit-Learn 25 H2O 25 XGBoost 26 DL Frameworks 26 Keras 26 Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O 27 Exploring the Ordinary Least-Squares Method 27 Scikit-Learn in Action 29 PySpark in Action 32 H2O in Action 34 Conclusion 40 Chapter 4: Survival Analysis with PySpark and Lifelines 41 Exploring Survival Analysis 41 Exploring Cox Proportional Hazards Method 41 Lifeline in Action 42 Exploring the Accelerated Failure Time Method 46 PySpark in Action 46 Conclusion 49 Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O 50 Exploring the Logistic Regression Method 50 Scikit-Learn in Action 52 PySpark in Action 59 H2O in Action 63 Conclusion 68 Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O 69 Decision Trees 69 Preprocessing Features 70 Scikit-Learn in Action 71 Gradient Boosting 76 XGBoost in Action 76 PySpark in Action 79 H2O in Action 81 Conclusion 84 Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O 85 Exploring Deep Learning 85 Multilayer Perceptron Neural Network 85 Preprocessing Features 86 Scikit-Learn in Action 87 Keras in Action 92 Deep Belief Networks 97 H2O in Action 97 Conclusion 98 Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H2O 99 Exploring the K-Means Method 99 Scikit-Learn in Action 101 PySpark in Action 103 H2O in Action 107 Conclusion 109 Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H2O 110 Exploring the Principal Component Method 110 Scikit-Learn in Action 111 PySpark in Action 114 H2O in Action 118 Conclusion 119 Chapter 10: Automating the Machine Learning Process with H2O 120 Exploring Automated Machine Learning 120 Preprocessing Features 121 H2O AutoML in Action 121 Conclusion 125 Index 126