By Antonio Gulli
BigData and computer studying in Python and Spark
Read Online or Download A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning PDF
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Additional info for A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning
From one hand this representation has an inherent error which can be reduced by carefully selecting a right set of representatives. From the other hand we might not want to create a too complex representation because it might be computationally expensive for the machine to learn a sophisticate model, indeed such model could possibly not generalize well to the unseen data. Real world data is noisy. We might have very few instances (outliers) which show a sensible difference from the majority of the remaining data, while the selected algorithm should be resilient enough to outliers.
Can you provide an example for Map and Reduce in Spark? (Let’s compute the Mean Square Error) Solution Code 11. Can you provide examples for other computations in Spark? Solution Code 12. How does Python interact with Spark Solution 13. What is Spark support for Machine Learning? Solution 14. How does Spark work in a parallel environment Solution Code 15. What is the mean, the variance, and the covariance? Solution Code 16. What are percentiles and quartiles? Solution Code 17. Can you transform an XML file into Python Pandas?
19] The following is an abstract from Spark API 40. What is a Linear Least Square Regression? Solution Linear models are simple and provide data partition based on straight lines. In general, they require a reasonably small amount of training data. More complex models, such as SVM with kernels and ensembles (which will be introduced in the next volume), allow data separation with more sophisticate curves - not only with straight lines - but they are in general more expensive to train, require more data, and are also more expensive when they predict results on unseen data.