A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning (A Collection of Programming Interview Questions) - Softcover

Gulli, Antonio

Softcover

ISBN 10: 1517216710 ISBN 13: 9781517216719

Publisher: CreateSpace Independent Publishing Platform, 2015

Synopsis

BigData and Machine Learning in Python and Spark

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Table of Content1. What are the most important machine learning techniques? 10Solution 102. Why is it important to have a robust set of metrics for machine learning? 11Solution 11Code 123. Why are Features extraction and engineering so important in machine learning? 12Solution 124. Can you provide an example of features extraction? 14Solution 14Code 145. What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? 15Solution 156. What is a Bias - Variance tradeoff? 16Solution 167. What is a cross-validation and what is an overfitting? 17Solution 17Code 188. Why are vectors and norms used in machine learning? 18Solution 18Code 199. What are Numpy, Scipy and Spark essential datatypes? 19Solution 19Code 2010. Can you provide an example for Map and Reduce in Spark? (Let's compute the Mean Square Error) 20Solution 20Code 2111. Can you provide examples for other computations in Spark? 22Solution 22Code 2512. How does Python interact with Spark 26Solution 2613. What is Spark support for Machine Learning? 26Solution 2614. How does Spark work in a parallel environment 27Solution 27Code 2715. What is the mean, the variance, and the covariance? 27Solution 27Code 2816. What are percentiles and quartiles? 28Solution 28Code 2817. Can you transform an XML file into Python Pandas? 29Solution 29Code 2918. Can you read HTML into Python Pandas? 30Solution 30Code 3019. Can you read JSON into Python Pandas? 31Solution 31Code 3120. Can you draw a function from Python? 31Solution 31Code 3121. Can you represent a graph in Python? 32Solution 32Code 3222. What is an Ipython notebook? 33Solution 33Code 3323. What is a convenient tool for performing data statistics? 34Solution 34Code 3424. How is it convenient to visualize data statistics 35Solution 35Code 3525. How to compute covariance and correlation matrices with pandas 36Solution 36Code 3626. Can you provide an example of connection to the Twitter API? 37Solution 37Code 3727. Can you provide an example of connection to the LinkedIn API? 39Solution 39Code 3928. Can you provide an example of connection to the Facebook API? 39Solution 39Code 4029. What is a TFxIDF? 40Solution 40Code 4030. What is "features hashing"? And why is it useful for BigData? 41Solution 4131. What is "continuous features binning"? 42Solution 4232. What is an LP normalization? 42Solution 42Code 4233. What is a Chi Square Selection? 42Solution 4234. What is mutual information and how can it be used for features selection? 43Solution 4335. What is a loss function, what are linear models, and what do we mean by regularization parameters in machine learning? 43Solution 4336. What is an odd ratio? 4637. What is a sigmoid function and what is a logistic function? 46Code 4738. What is a gradient descent? 47Solution 4739. What is a stochastic gradient descent? 49Solution 49Code 4940. What is a Linear Least Square Regression? 50Solution 50Code 5141. What are Lasso, Ridge, and ElasticNet regularizations? 52Solution 5242. What is a Logistic Regression? 52Solution 52Code 5343. What is a stepwise regression? 54Solution 5444. How to include nonlinear information into linear models 54Solution 5445. What is a Naïve Bayes classifier? 55Solution 5546. What is a Bernoulli and a Multivariate Naïve Bayes? 57Solution 57Code 5847. What is a Gaussian? 59Solution 59Code 5948. What is a Standard Scaling? 60Solution 60Code 6049. Why are statistical distributions important? 61Solution 61Code 6350. Can you compare your data with some distribution? What is a qq-plot? 63Solution 63Code 6351. What is a Gaussian Naïve Bayes? 64Solution 6452. What is another way to use Naïve Bayes with continuous data? 64Solution 6453. What is the Nearest Neighbor classification? 65Solution 65Code 6654. What are Support Vector Machines (SVM)? 66Solution 66Code 6855. What are SVM Kernel tricks? 68Solution 6856. What is K-Means Clustering? 70Solution 70Code 7157. Can you provide an example for Text Classification with Spark? 71Solution 71Code 7158. Where to go from here 72Appendix A 7559. Ultra-Quick introduction to Python 7560. Ultra-Quick introduction to Probabilities 7661. Ultra-Quick introduction to Matrices and Vectors 76

Edition details

• Publisher: CreateSpace Independent Publishing Platform
• Publication date: 2015
• ISBN 10: 1517216710
• ISBN 13: 9781517216719
• Binding: Paperback
• Language: English
• Number of pages: 84