实用数据科学和Python机器学习（影印版） [Hands-on Data Science and Python Machine Learning]这本书，是由东南大学出版社在2019-05-01月出版的，本书著作者是 Frank，Kane 著，此次本版是第1次印刷发行， 国际标准书号(ISBN)：9787564183202，品牌为南京东南大学出版社, 这本书的包装是16开平装，所用纸张为胶版纸，全书共有403页字数51万4000字， 是一本非常不错的Python编程书籍。
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分析数据并高效地执行机器学习。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最终数据分析。
My name is Frank Kane. I spent nine years at ******， 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.
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
Being a data scientist in the tech industry is one of the most rewarding careers on the planet today. I went and studied actual job descriptions for data scientist roles at tech companies and I distilled those requirements down into the topics that you'll see in this course. Hands-On Data Science and Python Machine Learning is really comprehensive. We'll start with a crash course on Python and do a review of some basic statistics and probability， but then we're going to dive right into over 60 topics in data mining and machine learning. That includes things such as Bayes' theorem， clustering， design trees， regression analysis， experimental design； we'll look at them all. Some of these topics are really fun.
We're going to develop an actual movie recommendation system using actual user movie rating data. We're going to create a search engine that actually works for Wikipedia data. We're going to build a spam classifier that can correctly classify spam and nonspam emails in your email account， and we also have a whole section on scaling this work up to a cluster that runs on big data using Apache Spark.
If you're a software developer or programmer looking to transition into a career in data saence， this course will teach you the hottest skills without all the mathematical notation and pretense that comes along with these topics. We're just going to explain these concepts and show you some Python code that actually works that you can dive in and mess around with to make those concepts sink home， and if you're working as a data analyst in the finance industry， this course can also teach you to make the transition into the tech industry. All you need is some prior experience in programming or scripting and you should be good to go.
The general format of this book is I'll start with each concept， explaining it in a bunch of sections and graphical examples. I will introduce you 1：o some of the notations and fancy terminologies that data scientists like to use so you can talk the same language， but the concepts themselves are generally pretty simple. After that， I'll throw you into some actual Python code that actually works that we can run and mess around with， and that will show you how to actually apply these ideas to actual data. These are going to be presented as IPython Notebook files， and that's a format where I can intermix code and notes surrounding the code that explain what's going on in the concepts. You can take these notebook files with you after going through this book and use that as a handy-quick reference later on in your career， and at the end of each concept， I'll encourage you to actually dive into that Python code， make some modifications， mess around with it， and just gain more familiarity bLyY getting hands-on and actually making some modifications， and seeing the effects they have.
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