ARTIFICIAL INTELLIGENCE AND PYTHON DAY 1 STANLEY LIANG, LASSONDE - - PowerPoint PPT Presentation

ARTIFICIAL INTELLIGENCE AND PYTHON

DAY 1 STANLEY LIANG, LASSONDE SCHOOL OF ENGINEERING, YORK UNIVERSITY

WHAT IS PYTHON

• An interpreted high-level programming language for general-purpose

programming.

• Python features a dynamic type system and automatic memory management.
• Python supports multiple programming paradigms, including object-oriented,

imperative, functional and procedural.

• Python as has a large and comprehensive standard library and multiple

packages for different purposes.

• In this course, we will use the Anaconda distribution with various Python

tools to implement AI and machine learning tasks

INSTALL SOFTWARE

• Download and install Python 3.6 from https://www.python.org/downloads/
• Visit https://anaconda.org/anaconda/python
• For PC with Windows 10: open a command line and type: conda install -c anaconda python
• For Mac, download from: https://www.anaconda.com/download/#macos
• Open Anaconda Navigator and launch Jupyter
• Jupyter is an interactive IDE (integrated development environment) for Python
• Other choice: PyCharm,

Visual Studio, Spyder, etc.

BASIC TYPE AND ASSIGNMENT

• String - unlike C, Python has no char
• Number - unlike C, Python has no int or float / double
• Boolean -True / False, capitalize the first letter
• Multiple Assignment
• The null value – None, not null

FLOW CONTROL

• Be careful of the indentation
• Branching: If-Then-Else
• Iteration: For-Loop, while-Loop, No native do-while-loop

DATA STRUCTURE

• Tuple - read-only collections of items
• List - use the square bracket notation and can be index using array notation
• Dictionary - are mappings of names to values, like key-value pairs. Note the use of the

curly

• Summary
• Tuple uses ( ), List uses [ ], Dictionary uses { } with ‘ ’ for the keys
• To subset, always use [ ]

FUNCTION IN PYTHON

• Function in Python is initiated by the keyword “def”, i.e. define
• Do not use “func” or “function” as the keyword, but remember use the parenthesis “( )”

as the sign of a “function call”

• The biggest tricky thing with Python is the whitespace.
• Ensure that you have an empty new line after indented code.
• A function can have one or more arguments, or have no arguments, but don’t need to

return a type

THE NUMPY

• NumPy provides the foundation data structures and operations for SciPy
• These are arrays (ndarrays) that are efficient to define and manipulate
• Before use, you need to import the numpy package
• If use Anaconda, the numpy is installed by default
• If Python cannot find it, use pip, or conda to install from commandline
• python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose

DATA VISUALIZATION

• In Python, we can visualize the data by the Matplotlib package
• Matplotlib can be used for creating plots and charts
• The general procedure to use Matplotlib
• import matplotlib.pyplot as plt
• Call a plotting function such as plt.plot( ) or plt.scatter, etc.
• Call the plot property configuration functions such as label, lim, etc.
• Call title, text, etc. to add notations
• Visualize the configured plot by show( )

PANDAS AND DATAFRAME

• Pandas provides data structures and functionality to quickly manipulate and

analyze data

• The two important element in Pandas
• Series - a one dimensional array of data where the rows are labeled using a time axis
• Subset a Series by index
• DataFrame - a multi-dimensional array where the rows and the columns can be labeled
• Subset a DataFrame by columns
• Subset a DataFrame by rows

LOAD DATA FROM A CSV FILE

• Before starting machine learning, you should load your data into Python
• The most common format for machine learning data is CSV files
• Three ways to load a CSV into Python
• Load CSV Files with the Python Standard Library
• Load CSV Files with NumPy
• Load CSV Files with Pandas (recommended)
• CSV from two source
• Local machine – always use ‘/’ to define the path
• From a URL – using urllib.request.urlopen or pandas.read_csv

UNDERSTAND YOUR DATA

• You must understand your data in order to get the best results
• Take a peek for a first impression
• Review the dimension of the dataset
• Review the data type of the attributes (columns)
• Summarize the distribution of instances across classes in your dataset
• Summarize your data using descriptive statistics
• Understand the relationships in your data using correlations
• Review the skew of the distributions of each attribute

VISUALIZE YOUR DATA

• You must understand your data in order to get the best results from machine learning algorithms.
• The intuitive way to learn more about your data is to visualize them.
• Plots for univariate (one variable)
• Histogram
• Density Plot
• Box & Whisker Plot
• Plot for Multivariate (more than one variable)
• Correlation Matrix Plot
• Scatter Plot Matrix

PREPARE YOUR DATA FOR MACHINE LEARNING

• Many machine learning algorithms make assumptions about your data
• Different algorithms requires different data transforms – data preprocessing
• Prepare the data to best expose the structure of the problem
• Rescale data
• Standardize data
• Normalize data
• Binarize data
• The scikit-learn library of Python provides two standard methods for transforming data
• Fit and Multiple Transform
• Combined Fit-And-Transform