By Antonio Gulli
BigData and desktop studying in Python and Spark
Read 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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Extra info for A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning
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? 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.
Authorization_code for getting access. get_connections() print connections 28. Can you provide an example of connection to the Facebook API? com/docs/graph-api/overview and the following code fragment provides basic usage for getting friends’ connections with the user’s profile and for posting them on the Facebook wall. put_object("me", "feed", message="Posting in my wall") 29. What is a TFxIDF? Solution TFxIDF is a weighting technique frequently used for text classifications. The key intuition is to boost a term which is frequent for a document d in a collection of documents (Term Frequency=TF) but is not so frequent in all the remaining documents in (Inverse Document Frequency, IDF).
Solution In machine learning a set of true labels is called the gold set. This set of examples is typically built manually either by human experts or via crowdsourcing with tools like the Amazon Mechanical Turk or via explicit/implicit feedback collected by users online. For instance: a gold set can contain news articles that are manually assigned to different categories by experts of the various subjects, or it might contain movies with associated rating provided by Netflix users, or ratings of images collected via crowdsourcing.