正版 实用数据科学和Python机器学习 Fran Kane著 东南大学出版社

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 Iists
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 element
List of tuples
Dictionaries
lterating 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 lPython，Jupyter Notebook
Running Python scripts in command prompt
Using the Canopy I DE
Summary
Chapter 2：Statistics and Probability Refresher，and Python Practice
Types of data
NumericaI data
Discrete data
Continuous data
Categorical data
OrdinaI data
Mean，median，and mode
Mean
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 variance
Try 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
……
Chapter 3：Matplotlib and Advanced Probability Concepts
ChantAr 4：Predictive ModeIs
Chapter 5：Machine Learning with Pvthon
Chapter 6：Recommender Systems
Chapter 7：More Data Mininq and Machine Learninq Techniaues
ChaDter 8：Dealing with Real．World Data
Chapter 9：Apache Spark-Machine Learning on Big Data
Chapter 10：Testing and Experimental Design
Index

弗兰克？凯恩，My name is Frank Kane. I spent nine years at amazon, corn and imdb. corn, wrangling millionsof customer ratings and customer transactions to produce things such as personalizedrecommendations for movies and products and "people who bought this also bought." I tellyou, I wish we had Apache Spark back then, when I spent years trying to solve theseproblems there. I hold 17 issued patents in the fields of distributed computing, data mining,and machine learning. In 2012, I left to start my own successful company, Sundog Software,which focuses on virtual reality environment technology, and teaching others about bigdata analysis.

　　从事Amazon和IMDB的机器学习算法相关工作的Frank Kane将指导你迈向数据科学世界的第一步。
　　《实用数据科学和Python机器学习（影印版）》为你提供了理解和探究该领域核心主题所需的工具，以及构建和分析你自己的机器学习模型的信心和实践。借助有趣易懂的实例，Frank Kane以任何人都能理解的方式解释了贝叶斯方法和K-means聚类等潜在的复杂主题。基于Frank大获成功的数据科学课程，《实用数据科学和Python机器学习（影印版）》将使你能够使用Python分析数据并地执行机器学习。Frank会使用Python所提供的各种数据挖掘和数据分析技术帮助你挖掘数据的价值，开发有效的预测模型来预测未来的结果。你还将学习到如何使用Apache Spark对大数据开展大规模的机器学习。书中涵盖了准备待分析的数据、训练机器学习模型以及可视化数据分析。