The Python training course will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, continues to small GUI programs. Participants will learn Python data types such as Tuples and Dictionaries, Looping, Functions and I/O handling. Python training will also give you an overview of analyze data,  Object Oriented Programming and Graphical application development. Python Scripting course will explain some basics modules and their usage. At the end of the Python Scripting Training, individuals will have the skills to grow in Web-Development, GUI Application Programming.

The training course will take place over three weeks starting from April 7, 2019 at 3 pm.  The training covering broad range of topics in different sessions. In theory sessions attendees will learn the features of Python, In the hands-on sessions, attendees will learn to interact with the Python and apply it effectively through interactive exercises and tutorials.The course will be taught by Dr.Polla Fatah.

Sessions will take place on every Sunday, Tuesday and Wednesday every week April 7, from 15:00 pm to 17:00 pm, in 212 Hall, Computer Engineering Department, Faculty of Engineering, Tishk International University(TIU). sessions are open to all participants and registration is required. Attendance to the hands-on sessions are limited to 20 participants.

Registration for the hands-on sessions is mandatory and open through , April 7,2019

Agenda

Day 1: This subject is about basis python programming, it in includes these concepts.

  • Output and inputs.
  • Conditional statement and Loops.

Day 2: This subject explains packages for data manipulation and visualization in python.

  • List comprehension.
  • NumPy and SciPy packages.
  • Pandas package.

Day 3: An introduction to the main methods of data mining and how to implement them using Sicki-learn package.

  • Introduction to data mining.
  • Scikit-learn package.
  • Association mining.

Day 4: Introduction to deep learning using TensorFlow.

  • Introduction to Neural networks.
  • Convolutional Neural networks.
  • Deep learning.
  • TensorFlow implementation for classification of images.