Hands-On Data Science and Python Machine Learning.
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...
Saved in:
| Online Access: |
Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2017
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
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| 100 | 1 | |a Kane, Frank. | |
| 245 | 1 | 0 | |a Hands-On Data Science and Python Machine Learning. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (415 pages) | ||
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| 505 | 0 | |a Copyright; Credits; About the Author; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started; Installing Enthought Canopy; Giving the installation a test run; If you occasionally get problems opening your IPNYB files; Using and understanding IPython (Jupyter) Notebooks; Python basics -- Part 1; Understanding Python code; Importing modules; Data structures; Experimenting with lists; Pre colon; Post colon; Negative syntax; Adding list to list; The append function; Complex data structures; Dereferencing a single element; The sort function; Reverse sort; Tuples. | |
| 505 | 8 | |a Dereferencing an elementList of tuples; Dictionaries; Iterating through entries; Python basics -- Part 2; Functions in Python; Lambda functions -- functional programming; Understanding boolean expressions; The if statement; The if-else loop; Looping; The while loop; Exploring activity; Running Python scripts; More options than just the IPython/Jupyter Notebook; Running Python scripts in command prompt; Using the Canopy IDE; Summary; Chapter 2: Statistics and Probability Refresher, and Python Practice; Types of data; Numerical data; Discrete data; Continuous data; Categorical data; Ordinal data. | |
| 505 | 8 | |a Mean, median, and modeMean; Median; The factor of outliers; Mode; Using mean, median, and mode in Python; Calculating mean using the NumPy package; Visualizing data using matplotlib; Calculating median using the NumPy package; Analyzing the effect of outliers; Calculating mode using the SciPy package; Some exercises; Standard deviation and variance; Variance; Measuring variance; Standard deviation; Identifying outliers with standard deviation; Population variance versus sample variance; The Mathematical explanation; Analyzing standard deviation and variance on a histogram. | |
| 505 | 8 | |a Using Python to compute standard deviation and varianceTry it yourself; Probability density function and probability mass function; The probability density function and probability mass functions; Probability density functions; Probability mass functions; Types of data distributions; Uniform distribution; Normal or Gaussian distribution; The exponential probability distribution or Power law; Binomial probability mass function; Poisson probability mass function; Percentiles and moments; Percentiles; Quartiles; Computing percentiles in Python; Moments; Computing moments in Python; Summary. | |
| 505 | 8 | |a Chapter 3: Matplotlib and Advanced Probability ConceptsA crash course in Matplotlib; Generating multiple plots on one graph; Saving graphs as images; Adjusting the axes; Adding a grid; Changing line types and colors; Labeling axes and adding a legend; A fun example; Generating pie charts; Generating bar charts; Generating scatter plots; Generating histograms; Generating box-and-whisker plots; Try it yourself; Covariance and correlation; Defining the concepts; Measuring covariance; Correlation; Computing covariance and correlation in Python; Computing correlation -- The hard way. | |
| 505 | 8 | |a Computing correlation -- The NumPy way. | |
| 520 | 8 | |a 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 efficientlyWho This Book Is ForIf 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 datasetsIn DetailJoin 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 approachThis comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time. | |
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| 650 | 0 | |a Data mining. | |
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