Before even starting to write this book, I asked myself a question: Is there a need for another book on Machine Learning? I mean that there are so many books written on this subject already that this might end up as just another book on the shelf. To find the answer, I spent a lot of time thinking and after a while, a few patterns started to emerge. The books that have been written on Machine Learning were too detailed and lacked a high-level overview. Most of these would start really easy but after a couple of chapters, it felt overwhelming to continue as the content became too deep. As a result, readers would give up without getting enough out of the book. That’s why I wanted to write this book, which demonstrates the different ways of using Machine Learning without getting too deep, yet capturing the complete methodology to build an ML model from scratch. The next obvious question was this: Why Machine Learning using PySpark? The answer to this question did not take too long since I am a practicing Data Scientist and well aware of the challenges faced by people dealing with data. Most of the packages or modules are often limited as they process data on a single machine. Moving from a development to production environment becomes a nightmare if ML models are not meant to handle Big Data, and finally the processing of data itself needs to be fast and scalable. For all these reasons, it made complete sense to write this book on Machine Learning using PySpark to understand the process of using Machine Learning from a Big Data standpoint.

Now we come to the core of the book Machine Learning with PySpark . This book is divided into three different sections. The first section gives the introduction to Machine Learning and Spark, the second section talks about Machine Learning in detail using Big Data, and finally the third part showcases Recommender Systems and NLP using PySpark. This book might also be relevant for Data Analysts and Data Engineers as it covers steps of Big Data processing using PySpark as well. The readers who want to make a transition to Data Science and the Machine Learning field would also find this book easier to start with and can gradually take up more complicated stuff later. The case studies and examples given in the book make it really easy to follow along and understand the fundamental concepts. Moreover, there are very few books available on PySpark out there, and this book would certainly add some value to the knowledge of the readers. The strength of this book lies in explaining the Machine Learning algorithms in the most simplistic ways and uses a practical approach toward building them using PySpark.

I have put in my entire experience and learning into this book and feel it is precisely relevant to what businesses are seeking out there to solve real challenges. I hope you have some useful takeaways from this book.