Description
Book SynopsisMigrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will LearnMaster the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learnWho This Book Is ForData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.
Table of ContentsChapter 1: An Easy Transition.- Chapter 2: Selecting Algorithms.- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark.- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark.- Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark.- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark.- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark.- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark.- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark.- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark.- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark.- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark.- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark.- Chapter 17: Pipelines with Scikit-Learn and PySpark.- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.
