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

This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

This book covers the in-memory, distributed cluster computing framework called PySpark, the machine learning framework platforms called Scikit-Learn, PySpark MLlib, H2O, and XGBoost, and the deep learning framework known as Keras. After reading this book, you will be able to apply supervised and unsupervised learning to solve practical and real-world data problems. In this book, you will learn how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning process.

To begin, the book carefully presents supervised and unsupervised ML and DL models and examines big data frameworks and machine learning and deep learning frameworks. It also discusses the parametric model called Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model and Accelerated Failure Time (AFT). It presents a binary classification model called Logistic Regression and an ensemble model called Gradient Boost Trees. It also introduces DL and an artificial neural network, the Multilayer Perceptron (MLP) classifier. It describes a way of performing cluster analysis using the k-means model. It explores dimension reduction techniques like Principal Components Analysis and Linear Discriminant Analysis and concludes by unpacking automated machine learning.

The book targets intermediate data scientists and machine learning engineers who want to learn how to apply key big data frameworks, as well as ML and DL frameworks. Before exploring the contents of this book, be sure that you understand basic statistics, Python programming, probability theories, and predictive analytics.