By Antonio Gulli
BigData and laptop studying in Python and Spark
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Extra resources for A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning
Collected) by applying a “reduce” operator. g. a user defined function). As an example of distributed computation let’s compute the Mean Square Error (MSE), the average of the squares of the difference between the estimator and what is estimated. In the following example we suppose thatvaluesAndPreds is an RDD of many ( tuples. Those are mapped into values . All intermediate results computed by parallel workers are then reduced by applying a sum operator. The final result is then divided by the total number of tuples as defined by the mathematical definition.
Note that Spark hides all the low level details to the programmer by allowing to write a distributed code which is very close to a mathematical formulation. The code adopts python lambda computation for compact representations of anonymous functions.  11. Can you provide examples for other computations in Spark? Solution The first code fragment is an example of map reduction, where we want to find the line with most words in a text. First each line is mapped into the number of words it contains.
Linear and Logistic regression are examples which will be discussed in the present volume. Decision Trees, SVM and Neural Networks can also be used for Regression. In Associative rule learning computers are presented with a large set of observations, all being made up of multiple variables. The task is then to learn relations between variables such us A & BC (if A and B happen, then C will also happen). In Clustering computers learn how to partition observations in various subsets, so that each partition will be made up of similar observations according to some well-defined metric.
A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning by Antonio Gulli