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Introduction to Artificial Intelligence with Python Programming
course information

Artificial intelligence is the intelligence demonstrated by machines, in contrast to the intelligence displayed by humans. This course covers the basic concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc., and its implementation in Python.

  • Python is a general-purpose programming language that is becoming more and more popular for doing data science and artificial intelligence based applications.
  • At the beginning of the course, you will learn basic syntax, operators, variables, data types, String, data structures like List, tuples, Dictionaries, Sets, etc.
  • Later part of the course is Object Oriented Python, Exception handling, File handling and GUI Programming
  • As part of this course you will get complete hands on experience on IMP data science and AI related Python libraries like NumPy, Pandas, Matplotlib, SciPy, etc.
course objectives

At the end of the training program, participants will learn to:

  • Understand the importance, principles, and fields of AI
  • Work with Anaconda and Spider IDE to develop Python applications
  • Learn core Python scripting elements such as variables and flow control structures, looping statements
  • Discover how to work with lists and sequence data
  • Write Python functions to facilitate code reuse
  • Make code robust by handling errors and exceptions properly
  • Explore Python’s object-oriented features
  • Create GUI applications
  • Work with the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
  • Work with Numpy library which is the foundation of all the other analytical tools in Python.
  • Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
  • Produce informative, useful and beautiful visualizations for analyzing data
  • Perform common statistical calculations and use the results to reach conclusions about the data.
course outline

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
CERTIFICATION

DISS CERTIFICATE

          DATE          TIME
QatarOman
10 May 202011:00 to 13:0012:00 to 14:00
13 June 202011:00 to 13:0012:00 to 14:00
11 July 202011:00 to 13:0012:00 to 14:00
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TESTIMONIAL

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