Data science solutions with Python : fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn / Tshepo Chris Nokeri.

Format:

Book

Author/Creator:

Nokeri, Tshepo Chris, author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (128 pages)

Place of Publication:

[Place of publication not identified] : Apress, [2022]

Summary:

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.

Contents:

Intro

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

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

Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O

Exploring the Logistic Regression Method

Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O

Decision Trees

Preprocessing Features

Gradient Boosting

XGBoost in Action

Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O.

Exploring Deep Learning

Multilayer Perceptron Neural Network

Keras in Action

Deep Belief Networks

Chapter 8: Cluster Analysis with Scikit-Learn, PySpark, and H2O

Exploring the K-Means Method

Chapter 9: Principal Component Analysis with Scikit-Learn, PySpark, and H2O

Exploring the Principal Component Method

Chapter 10: Automating the Machine Learning Process with H2O

Exploring Automated Machine Learning

H2O AutoML in Action

Index.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781523150984

152315098X

9781484277621

1484277627

OCLC:

1283854399