June 2017
Beginner to intermediate
296 pages
7h 4m
English
Content preview from Frank Kane's Taming Big Data with Apache Spark and Python
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MLlib capabilities
The following is a list of different features of MLlib. They have support in the library to help you with these various techniques:
- Feature extraction
- Term Frequency / Inverse Document frequency useful for search
- Basic statistics
- Chi-squared test, Pearson or Spearman correlation, min, max, mean, and variance
- Linear regression and logistic regression
- Support Vector Machines
- Naïve Bayes classifier
- Decision trees
- K-Means clustering
- Principal component analysis and singular value decomposition
- Recommendation using Alternating Least Squares
I don't really have time to go into what machine learning is and what all these different techniques use in a lot of depth, but this should mean something to you if you're coming ...
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