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 |
Table of Contents:
- 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.
- 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.
- 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.
- 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.
- 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.
- Computing correlation
- The NumPy way.
