دانلود کتاب یادگیری ماشینی عملی با اسپارک

Since 1964, from the beginning of automation and intelligence towards machines, the applications of machine learning (ML) have made tremendous progress during the last two decades. But still, there is a large scope of improvement for fast and accurate decisions. The aim of the present book is to make the readers aware of day-to-day activities that make life smarter and cosier with the use of ML applications using Apache Spark. Initially, there was a single processing framework used in ML to solve the critical problems. Due to the standalone processing, the training and testing of models usually takes more time and requires more resources. Also, the problem becomes more complex and time-consuming for big data (high dimension and data volume of features) in ML. Therefore, a promising inmemory analytics layer needs to be introduced, such as Apache Spark, for handling and training the heavy intelligence model in an optimised manner. Generally, there are two types of distributed frameworks, like Apache Hadoop and Apache Spark. Due to some limitations in Hadoop, most MNCs later adopted Apache Spark. This book contains comprehensive and lucid details from scratch to production level implementation of a distributed framework, which the readers will find useful. Also, readers will learn to easily transition from conceptual scenarios to practical implementation and get educated them about the various components of ML pipelines using Apache Spark. Although a Github link is provided in this book where the reader can try the practical stuff using those codebases.