ARTIFICIAL INTELLIGENCE AND PYTHON

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1 ARTIFICIAL INTELLIGENCE AND PYTHON DAY 1 STANLEY LIANG, LASSONDE SCHOOL OF ENGINEERING, YORK UNIVERSITY

2 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

3 INSTALL SOFTWARE Download and install Python 3.6 from Visit For PC with Windows 10: open a command line and type: conda install -c anaconda python For Mac, download from: Open Anaconda Navigator and launch Jupyter Jupyter is an interactive IDE (integrated development environment) for Python Other choice: PyCharm, Visual Studio, Spyder, etc.

4 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

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

6 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 [ ]

7 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

8 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

9 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( )

10 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

11 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

12 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

13 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

14 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