By Antonio Gulli
BigData and desktop studying in Python and Spark
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Extra info for A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning
What is a Standard Scaling? Solution Code 49. Why are statistical distributions important? Solution Code 50. Can you compare your data with some distribution? What is a qq-plot? Solution Code 51. What is a Gaussian Naïve Bayes? Solution 52. What is another way to use Naïve Bayes with continuous data? Solution 53. What is the Nearest Neighbor classification? Solution Code 54. What are Support Vector Machines (SVM)? Solution Code 55. What are SVM Kernel tricks? Solution 56. What is K-Means Clustering?
First each line is mapped into the number of words it contains. Then those numbers are reduced and the maximum is taken. Pretty simple: one single line of code stays here for something which requires hundreds of lines in other parallel paradigms such as Hadoop. Spark supports two types of operations: transformations, which create a new RDD dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. All transformations in Spark are lazy because the computation is postponed as much as possible until the results are really needed by the program.
What is the Nearest Neighbor classification? Solution Code 54. What are Support Vector Machines (SVM)? Solution Code 55. What are SVM Kernel tricks? Solution 56. What is K-Means Clustering? Solution Code 57. Can you provide an example for Text Classification with Spark? Solution Code 58. Where to go from here Appendix A 59. Ultra-Quick introduction to Python 60. Ultra-Quick introduction to Probabilities 61. Ultra-Quick introduction to Matrices and Vectors 1. What are the most important machine learning techniques?
A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning by Antonio Gulli