Module 1: An Overview of Artificial Intelligence

• Basic Concept of Artificial Intelligence (AI)
• The Necessity of Learning AI
• Types of Intelligence
• What’s involved in AI
• Application of AI
• Cognitive Modeling: Simulating Human Thinking Procedure
• Agent & Environment
• Python for AI Applications

Module 2: Why Python for AI

• An Overview of Python
• Interpreted languages
• Advantages and disadvantages
• Downloading and installing
• Which version of Python
• Where to find documentation

Module 3: The python environment, Anaconda and Spyder IDE

• Structure of a Python script
• Using the interpreter interactively
• Install Anaconda
• Start working with Spyder IDE

Module 4: Getting Started with Data types, Operators, Variables

• Using variables
• String types: normal, raw and Unicode
• String operators and expressions
• Math operators and expressions
• Writing to the screen
• Command line parameters
• Reading from the keyboard
• Flow Control and Loops
• About flow control

Module 5: Indenting is significant

• The if and elif statements
• while loops
• Using lists
• Using the for statement
• The range() function

Module 6: Array types

• list operations
• list methods
• Strings are special kinds of lists
• tuples
• sets

Module 7: Dictionaries

• Dictionaries and Sets
• Dictionary overview
• Creating dictionaries
• Dictionary functions
• Fetching keys or values
• Testing for existence of elements
• Deleting elements

Module 8: Working with Files

• Text/csv file I/O overview
• Opening a text/csv file
• Reading text /csv files
• Raw (binary) data
• Using the pickle module
• Writing to a text/csv file

Module 9: Modular Programming with functions

• Need of modular programming
• Syntax of function definition
• Formal parameters
• Global versus local variables
• Passing parameters and returning values

Module 10: Errors and Exception Handling

• Dealing with syntax errors
• Exception classes
• Handling exceptions with try/except
• Cleaning up with finally
• Raise exceptions

Module 11: Modules and Packages

• What is a module?
• The import statement
• Function aliases
• Packages

Module 12: Highlights of the Standard Library

• Working with the operating system
• Grabbing web pages
• Sending email
• Using glob for filename wildcards
• math and random
• Accessing dates and times with datetime
• Working with compressed files

Module 13: Object Oriented Programming with Python

• About o-o programming
• Defining classes
• Constructors
• Instance methods
• Instance data
• Class methods and data
• Destructors
• Inheritance, method overloading
• Data hiding and data encapsulation

Module 14: GUI Programming with PyQt

• Overview
• Qt Architecture
• Using designer
• Standard widgets
• Event handling

Module 15: Database Access

• The DB API
• Available Interfaces
• Connecting to a server
• Creating and executing a cursor
• Fetching data
• Parameterized statements

Module 16: Data analytics using NumPy

• NumPy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks
• Random number generation

Module 17: Data analytics using Pandas

• Pandas overview
• Series and Dataframes
• Reading and writing data
• Advanced indexing and slicing
• Merging and joining data sets
• Analyzing Datasets
• Sorting data
• Filtering values
• Basic statistics
• Leveraging SciPy/NumPy
• Using pandas Group-by plotting
• Handling missing data in pandas

Module 18: Data visualization using Matplotlib & Pandas visualization

• Creating a basic plot
• Commonly used plots
• Customizing styles
• Ad hoc data visualization
• Advanced usage
• Saving images

Module 19: Python visualization: Matplotlib and seaborn

• Pandas visualization: histograms, bar and box plots
• Pandas visualization: Scatter plots and pie charts
• Group-by plotting
• Pandas plot formatting