Python 机器学习

$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

$ brew update

$ brew install python3

C:\>jupyter notebook

pip install jupyter

import numpy as np

pip install NumPy

import pandas as pd

pip install Pandas

In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: data = np.array(['g','a','u','r','a','v'])
In [4]: s = pd.Series(data)
In [5]: print (s)
0    g
1    a
2    u
3    r
4    a
5    v
dtype: object

from sklearn.datasets import load_breast_cancer

pip install -U scikit-learn

2. Machine Learning with Python Machine
 – Python
 Learning Ecosystem
 with Python
An Introduction to Python
Python is a popular object-oriented programing language having the capabilities of high-
level programming language. Its easy to learn syntax and portability capability makes it
popular these days. The followings facts gives us the introduction to Python:

Python was developed by Guido van Rossum at Stichting Mathematisch Centrum in
the Netherlands.


It was written as the successor of programming language named ‘ABC’.
It’s first version was released in 1991.

The name Python was picked by Guido van Rossum from a TV show named Monty
Python’s Flying Circus.

 It is an open source programming language which means that we can freely
download it and use it to develop programs. It can be downloaded from
www.python.org.

Python programming language is having the features of Java and C both. It is
having the elegant ‘C’ code and on the other hand, it is having classes and objects
like Java for object-oriented programming.

It is an interpreted language, which means the source code of Python program
would be first converted into bytecode and then executed by Python virtual
machine.
Strengths and Weaknesses of Python
Every programming language has some strengths as well as weaknesses, so does Python
too.
Strengths
According to studies and surveys, Python is the fifth most important language as well as
the most popular language for machine learning and data science. It is because of the
following strengths that Python has:
Easy to learn and understand: The syntax of Python is simpler; hence it is relatively
easy, even for beginners also, to learn and understand the language.
Multi-purpose language: Python is a multi-purpose programming language because it
supports structured programming, object-oriented programming as well as functional
programming.
6
Machine Learning with Python
Huge number of modules: Python has huge number of modules for covering every
aspect of programming. These modules are easily available for use hence making Python
an extensible language.
Support of open source community: As being open source programming language,
Python is supported by a very large developer community. Due to this, the bugs are easily
fixed by the Python community. This characteristic makes Python very robust and
adaptive.
Scalability: Python is a scalable programming language because it provides an improved
structure for supporting large programs than shell-scripts.
Weakness
Although Python is a popular and powerful programming language, it has its own weakness
of slow execution speed.
The execution speed of Python is slow as compared to compiled languages because Python
is an interpreted language. This can be the major area of improvement for Python
community.
Installing Python
For working in Python, we must first have to install it. You can perform the installation of
Python in any of the following two ways:

 Installing Python individually

 Using Pre-packaged Python distribution: Anaconda
Let us discuss these each in detail.
Installing Python Individually
If you want to install Python on your computer, then then you need to download only the
binary code applicable for your platform. Python distribution is available for Windows,
Linux and Mac platforms.
The following is a quick overview of installing Python on the above-mentioned platforms:
On Unix and Linux platform
With the help of following steps, we can install Python on Unix and Linux platform:

 First, go to https://www.python.org/downloads/.

 Next, click on the link to download zipped source code available for Unix/Linux.

 Now, Download and extract files.

 Next, we can edit the Modules/Setup file if we want to customize some options.
1. Next, write the command run ./configure script
2. make
3. make install
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Machine Learning with Python
On Windows platform
With the help of following steps, we can install Python on Windows platform:

 First, go to https://www.python.org/downloads/.

 Next, click on the link for Windows installer python-XYZ.msi file. Here XYZ is the
version we wish to install.

 Now, we must run the file that is downloaded. It will take us to the Python install
wizard, which is easy to use. Now, accept the default settings and wait until the
install is finished.
On Macintosh platform
For Mac OS X, Homebrew, a great and easy to use package installer is recommended to
install Python 3. In case if you don't have Homebrew, you can install it with the help of
following command:
$ ruby -e "$(curl -fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)"
It can be updated with the command below:
$ brew update
Now, to install Python3 on your system, we need to run the following command:
$ brew install python3
Using Pre-packaged Python Distribution: Anaconda
Anaconda is a packaged compilation of Python which have all the libraries widely used in
Data science. We can follow the following steps to setup Python environment using
Anaconda:
Step1: First, we need to download the required installation package from Anaconda
distribution. The link for the same is https://www.anaconda.com/distribution/. You can
choose from Windows, Mac and Linux OS as per your requirement.
Step2: Next, select the Python version you want to install on your machine. The latest
Python version is 3.7. There you will get the options for 64-bit and 32-bit Graphical installer
both.
Step3: After selecting the OS and Python version, it will download the Anaconda installer
on your computer. Now, double click the file and the installer will install Anaconda package.
Step4: For checking whether it is installed or not, open a command prompt and type
Python as follows:
8
Machine Learning with Python
You
 can
 also
 check
 this
 in
 detailed
 video
 lecture
 at
https://www.tutorialspoint.com/python_essentials_online_training/getting_started_with_
anaconda.asp.
Why Python for Data Science?
Python is the fifth most important language as well as most popular language for Machine
learning and data science. The following are the features of Python that makes it the
preferred choice of language for data science:
Extensive set of packages
Python has an extensive and powerful set of packages which are ready to be used in
various domains. It also has packages like numpy, scipy, pandas, scikit-learn etc.
which are required for machine learning and data science.
Easy prototyping
Another important feature of Python that makes it the choice of language for data science
is the easy and fast prototyping. This feature is useful for developing new algorithm.
Collaboration feature
The field of data science basically needs good collaboration and Python provides many
useful tools that make this extremely.
One language for many domains
A typical data science project includes various domains like data extraction, data
manipulation, data analysis, feature extraction, modelling, evaluation, deployment and
updating the solution. As Python is a multi-purpose language, it allows the data scientist
to address all these domains from a common platform.
9
Machine Learning with Python
Components of Python ML Ecosystem
In this section, let us discuss some core Data Science libraries that form the components
of Python Machine learning ecosystem. These useful components make Python an
important language for Data Science. Though there are many such components, let us
discuss some of the importance components of Python ecosystem here:
Jupyter Notebook
Jupyter notebooks basically provides an interactive computational environment for
developing Python based Data Science applications. They are formerly known as ipython
notebooks. The following are some of the features of Jupyter notebooks that makes it one
of the best components of Python ML ecosystem:

Jupyter notebooks can illustrate the analysis process step by step by arranging the
stuff like code, images, text, output etc. in a step by step manner.



It helps a data scientist to document the thought process while developing the
analysis process.
One can also capture the result as the part of the notebook.
With the help of jupyter notebooks, we can share our work with a peer also.
Installation and Execution
If you are using Anaconda distribution, then you need not install jupyter notebook
separately as it is already installed with it. You just need to go to Anaconda Prompt and
type the following command:
C:\>jupyter notebook
10
Machine Learning with Python
After pressing enter, it will start a notebook server at localhost:8888 of your computer. It is
shown in the following screen shot:
Now, after clicking the New tab, you will get a list of options. Select Python 3 and it will
take you to the new notebook for start working in it. You will get a glimpse of it in the
following screenshots:
11
Machine Learning with Python
On the other hand, if you are using standard Python distribution then jupyter notebook
can be installed using popular python package installer, pip.
pip install jupyter
Types of Cells in Jupyter Notebook
The following are the three types of cells in a jupyter notebook:
Code cells: As the name suggests, we can use these cells to write code. After writing the
code/content, it will send it to the kernel that is associated with the notebook.
Markdown cells: We can use these cells for notating the computation process. They can
contain the stuff like text, images, Latex equations, HTML tags etc.
Raw cells: The text written in them is displayed as it is. These cells are basically used to
add the text that we do not wish to be converted by the automatic conversion mechanism
of jupyter notebook.
For
 more
 detailed
 study
 of
 jupyter
 notebook,
 you
 can
 go
 to
 the
 link
https://www.tutorialspoint.com/jupyter/index.htm.
NumPy
It is another useful component that makes Python as one of the favorite languages for
Data Science. It basically stands for Numerical Python and consists of multidimensional
array objects. By using NumPy, we can perform the following important operations:


Mathematical and logical operations on arrays.
Fourier transformation
12
Machine Learning with Python

 Operations associated with linear algebra.
We can also see NumPy as the replacement of MatLab because NumPy is mostly used along
with Scipy (Scientific Python) and Mat-plotlib (plotting library).
Installation and Execution
If you are using Anaconda distribution, then no need to install NumPy separately as it is
already installed with it. You just need to import the package into your Python script with
the help of following:
import numpy as np
On the other hand, if you are using standard Python distribution then NumPy can be installed
using popular python package installer, pip.
pip install NumPy
After installing NumPy, you can import it into your Python script as you did above.
For
 more
 detailed
 study
 of
 NumPy,
 you
 can
 go
 to
 the
 link
https://www.tutorialspoint.com/numpy/index.htm.
Pandas
It is another useful Python library that makes Python one of the favorite languages for
Data Science. Pandas is basically used for data manipulation, wrangling and analysis. It
was developed by Wes McKinney in 2008. With the help of Pandas, in data processing we
can accomplish the following five steps:

 Load

 Prepare

 Manipulate

 Model

 Analyze
Data representation in Pandas
The entire representation of data in Pandas is done with the help of following three data
structures:
Series: It is basically a one-dimensional ndarray with an axis label which means it is like a
simple array with homogeneous data. For example, the following series is a collection of
integers 1,5,10,15,24,25...
1
 5
 10
 15
 24
 25
 28
 36
 40
 89
Data frame: It is the most useful data structure and used for almost all kind of data
representation and manipulation in pandas. It is basically a two-dimensional data structure
which can contain heterogeneous data. Generally, tabular data is represented by using
13
Machine Learning with Python
data frames. For example, the following table shows the data of students having their
names and roll numbers, age and gender:
Name
 Roll number
 Age
 Gender
Aarav
 1
 15
 Male
Harshit
 2
 14
 Male
Kanika
 3
 16
 Female
Mayank
 4
 15
 Male
Panel: It is a 3-dimensional data structure containing heterogeneous data. It is very
difficult to represent the panel in graphical representation, but it can be illustrated as a
container of DataFrame.
The following table gives us the dimension and description about above mentioned data
structures used in Pandas:
Data Structure
 Dimension
 Description
Series
 1-D
 Size
 immutable,
 1-D
homogeneous data
DataFrames
 2-D
 Size
 Mutable,
Heterogeneous data in
tabular form
Panel
 3-D
 Size-mutable
 array,
container
 of
DataFrame.
We can understand these data structures as the higher dimensional data structure is the
container of lower dimensional data structure.
Installation and Execution
If you are using Anaconda distribution, then no need to install Pandas separately as it is
already installed with it. You just need to import the package into your Python script with
the help of following:
import pandas as pd
On the other hand, if you are using standard Python distribution then Pandas can be installed
using popular python package installer, pip.
pip install Pandas
After installing Pandas, you can import it into your Python script as did above.
14
Machine Learning with Python
Example
The following is an example of creating a series from ndarray by using Pandas:
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: data = np.array(['g','a','u','r','a','v'])
In [4]: s = pd.Series(data)
In [5]: print (s)
0
 g
1
 a
2
 u
3
 r
4
 a
5
 v
dtype: object
For
 more
 detailed
 study
 of
 Pandas
 you
 can
 go
 to
 the
 link
https://www.tutorialspoint.com/python_pandas/index.htm.
Scikit-learn
Another useful and most important python library for Data Science and machine learning
in Python is Scikit-learn. The following are some features of Scikit-learn that makes it so useful:

 It is built on NumPy, SciPy, and Matplotlib.

 It is an open source and can be reused under BSD license.

 It is accessible to everybody and can be reused in various contexts.

 Wide range of machine learning algorithms covering major areas of ML like
classification, clustering, regression, dimensionality reduction, model selection etc.
can be implemented with the help of it.
Installation and Execution
If you are using Anaconda distribution, then no need to install Scikit-learn separately as it is
already installed with it. You just need to use the package into your Python script. For
example, with following line of script we are importing dataset of breast cancer patients
from Scikit-learn:
15
Machine Learning with Python
from sklearn.datasets import load_breast_cancer
On the other hand, if you are using standard Python distribution and having NumPy and
SciPy then Scikit-learn can be installed using popular python package installer, pip.
pip install -U scikit-learn
After installing Scikit-learn, you can use it into your Python script as you have done above.