What do you think?
Rate this book
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
Chapter 1: Evolution of DataChapter 2: Introduction to Machine LearningChapter 3: Data ProcessingChapter 4: Linear RegressionChapter 5: Logistic Regression Chapter 6: Random ForestsChapter 7: Recommender SystemsChapter 8: ClusteringChapter 9: Natural Language Processing
241 pages, Paperback
Published December 15, 2018
12 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
January 28, 2020
Depending on your goals, this book could be good, or a waste of time:
If your goal is to have a really quick and super simple recap of what each of the main algorithms in ML does, and how to equally quickly and simply implement them with PySpark (on really simple datasets, many of them made up), then this is your book. It is very easy to read, and the author goes right to the point, in the explanations and the implementations.
If you are not in a hurry and your goal is to learn a lot about PySpark and its details, or to learn how to implement a comprehensive and nuanced ML pipeline, do some detailed EDA, build models, evaluate them and improve them, all this on some real datasets to answer interesting questions... then this is NOT your book. In this case I'd recommend "Learning Pyspark", by Tomasz Drabas and Denny Lee. Far more comprehensive, well-explained and complex.
Having said that, this is a good book to have if you just want to do a mini-PoC of each main ML algorithm with a notebook in a couple of afternoons.
If your goal is to have a really quick and super simple recap of what each of the main algorithms in ML does, and how to equally quickly and simply implement them with PySpark (on really simple datasets, many of them made up), then this is your book. It is very easy to read, and the author goes right to the point, in the explanations and the implementations.
If you are not in a hurry and your goal is to learn a lot about PySpark and its details, or to learn how to implement a comprehensive and nuanced ML pipeline, do some detailed EDA, build models, evaluate them and improve them, all this on some real datasets to answer interesting questions... then this is NOT your book. In this case I'd recommend "Learning Pyspark", by Tomasz Drabas and Denny Lee. Far more comprehensive, well-explained and complex.
Having said that, this is a good book to have if you just want to do a mini-PoC of each main ML algorithm with a notebook in a couple of afternoons.
Displaying 1 of 1 review