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Machine Learning with PySpark : With Natural Language Processing and Recommender Systems
Chapter 1: Evolution of Data
Chapter 2: Introduction to Machine Learning
Chapter 3: Data Processing
Chapter 4: Linear Regression
Chapter 5: Logistic Regression Chapter 6: Random Forests
Chapter 7: Recommender Systems
Chapter 8: Clustering
Chapter 9: Natural Language Processing
Chapter 2: Introduction to Machine Learning
Chapter 3: Data Processing
Chapter 4: Linear Regression
Chapter 5: Logistic Regression Chapter 6: Random Forests
Chapter 7: Recommender Systems
Chapter 8: Clustering
Chapter 9: Natural Language Processing
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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