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Introduction to Artificial Intelligence with Python Programming

overview

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 details
COURSE DURATIONLANGUAGECERTIFICATE
40 Hours – 2 Hours per DayEnglishDISS Certificate
 *Please notify us if an interpretation is required 
course benefits/progression opportunities
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 IntelligenceModule 2: Why Python for AIModule 3: The python environment, Anaconda and Spyder IDEModule 4: Getting Started with Data types, Operators, VariablesModule 5: Indenting is significant
  • Basic Concept of Artificial Intelligence (AI)
  • The Necessity of Learning AI
  • Types of Intelligence
  • What’s involved in AI
  • Application of AI
  • Cognitive Modelling: Simulating Human Thinking Procedure
  • Agent & Environment
  • Python for AI Applications
  • An Overview of Python
  • Interpreted languages
  • Advantages and disadvantages
  • Downloading and installing
  • Which version of Python
  • Where to find documentation
  • Structure of a Python script
  • Using the interpreter interactively
  • Install Anaconda
  • Start working with Spyder IDE
  • 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
  • The if and elif statements
  • while loops
  • Using lists
  • Using the for statement
  • The range() function
Module 6: Array typesModule 7: DictionariesModule 8: Working with FilesModule 9: Modular Programming with functionsModule 10: Errors and Exception Handling
  • list operations
  • list methods
  • Strings are special kinds of lists
  • tuples
  • sets
  • Dictionaries and Sets
  • Dictionary overview
  • Creating dictionaries
  • Dictionary functions
  • Fetching keys or values
  • Testing for existence of elements
  • Deleting elements
  • 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
  • Need of modular programming
  • Syntax of function definition
  • Formal parameters
  • Global versus local variables
  • Passing parameters and returning values
  • Dealing with syntax errors
  • Exception classes
  • Handling exceptions with try/except
  • Cleaning up with finally
  • Raise exceptions
Module 11: Modules and PackagesModule 12: Highlights of the Standard LibraryModule 13: Object Oriented Programming with PythonModule 14: GUI Programming with PyQtModule 15: Database Access
  • What is a module?
  • The import statement
  • Function aliases
  • Packages
  • 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
  • About o-o programming
  • Defining classes
  • Constructors
  • Instance methods
  • Instance data
  • Class methods and data
  • Destructors
  • Inheritance, method overloading
  • Data hiding and data encapsulation
  • Overview
  • Qt Architecture
  • Using designer
  • Standard widgets
  • Event handling
  • The DB API
  • Available Interfaces
  • Connecting to a server
  • Creating and executing a cursor
  • Fetching data
  • Parameterized statements
Module 16: Data analytics using NumPyModule 17: Data analytics using PandasModule 18: Data visualization using Matplotlib & Pandas visualizationModule 19: Python visualization: Matplotlib and seaborn
  • NumPy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks
  • Random number generation
  • Pandas overview
  • Series and Data frames
  • Reading and writing data
  • Advanced indexing and slicing
  • Merging and joining data sets
  • Analysing Datasets
  • Sorting data
  • Filtering values
  • Basic statistics
  • Leveraging SciPy/NumPy
  • Using pandas Group-by plotting
  • Handling missing data in pandas
  • Creating a basic plot
  • Commonly used plots
  • Customizing styles
  • Ad hoc data visualization
  • Advanced usage
  • Saving images
  • Pandas visualization: histograms, bar and box plots
  • Pandas visualization: Scatter plots and pie charts
  • Group-by plotting
  • Pandas plot formatting

 

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Brochure
Course Calendar

COURSE TYPE: CLASSROOM

DATEDAYQATAR TIME
7-11 Mar 21Sun-Thu09:00-15:00
11-15 Apr 21Sun-Thu09:00-15:00
9-13 May 21Sun-Thu09:00-15:00
13-17 Jun 21Sun-Thu09:00-15:00
11-15 Jul 21Sun-Thu09:00-15:00
8-12 Aug 21Sun-Thu09:00-15:00
12-16 Sep 21Sun-Thu09:00-15:00
10-14 Oct 21Sun-Thu09:00-15:00
14-18 Nov 21Sun-Thu09:00-15:00
12-16 Dec 21Sun-Thu09:00-15:00
TESTIMONIAL

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