Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark - Kane, Frank (Birmingham, UK : Packt Publishing, 2017)
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehen ...
Some MLlib Capabilities -- Special MLlib data types -- The vector data type -- LabeledPoint data type -- Rating data type -- Decision Trees in Spark with MLlib -- Exploring decision trees code -- Creating the SparkContext -- Importing and cleaning our data -- Creating a test candidate and building our decision tree -- Running the script -- K-Means Clustering in Spark -- Within set sum of squared errors (WSSSE) -- Running the code -- TF-IDF -- TF-IDF in practice -- Using TF- IDF -- Searching wikipedia with Spark MLlib -- Import statements -- Creating the initial RDD -- Creating and transforming a HashingTF object -- Computing the TF-IDF score -- Using the Wikipedia search engine algorithm -- Running the algorithm -- Using the Spark 2.0 DataFrame API for MLlib -- How Spark 2.0 MLlib works -- Implementing linear regression -- Summary -- Chapter 10: Testing and Experimental Design -- A/B testing concepts -- A/B tests -- Measuring conversion for A/B testing -- How to attribute conversions -- Variance is your enemy -- T-test and p-value -- The t-statistic or t-test -- The p-value -- Measuring t-statistics and p-values using Python -- Running A/B test on some experimental data -- When there's no real difference between the two groups -- Does the sample size make a difference? -- Sample size increased to six-digits -- Sample size increased seven-digits -- A/A testing -- Determining how long to run an experiment for -- A/B test gotchas -- Novelty effects -- Seasonal effects -- Selection bias -- Auditing selection bias issues -- Data pollution -- Attribution errors -- Summary -- Index.
Online resource; title from title page (Safari, viewed August 18, 2017).
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Keywords: Spark (Electronic resource : Apache Software Foundation); Spark (Electronic resource : Apache Software Foundation); Python (Computer program language); Machine learning; Data mining; Artificial intelligence; Data Mining; Artificial Intelligence; Machine Learning; Python (Langage de programmation); Apprentissage automatique; Exploration de données (Informatique); Intelligence artificielle; artificial intelligence; COMPUTERS -- Programming -- General; Artificial intelligence; Data mining; Machine learning; Python (Computer program language)