Introduction to Python Programming and to Red Hat OpenShift AI

As a result of attending this course, you will understand the foundations of the Red Hat OpenShift AI architecture. You will be able to organize code and configuration by using data science projects, workbenches, and data connections. You will also be able to execute and test code interactively by using Jupyter notebooks. This course is the starting point for the AI/ML learning path in which you will learn how to create and maintain AI/ML workflows. Course Content Summary

• Basics of Python syntax, functions and data types
• How to debug Python scripts using the Python debugger (pdb)
• Use Python data structures like dictionaries, sets, tuples and lists to handle compound data
• Learn Object-oriented programming in Python and Exception Handling
• How to read and write files in Python and parse JSON data
• How to effectively structure large Python programs using modules and namespaces
• Introduction to Red Hat OpenShift AI
• Data Science Projects
• Jupyter Notebooks

• Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models
• Developers who want to build and integrate AI/ML enabled applications
• MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI

• Experience with Git is required
• Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course
• Basic experience in the AI, data science, and machine learning fields is recommended
Technology considerations
• No ILT classroom will be available

An Overview of Python 3

• Introduction to Python and setting up the developer environment

Basic Python Syntax

• Explore the basic syntax and semantics of Python

Language Components

• Understand the basic control flow features and operators

Collections

• Write programs that manipulate compound data using lists, sets, tuples and dictionaries

Functions

• Decompose your programs into composable functions

Modules

• Organize your code using Modules for flexibility and reuse

Classes in Python

• Explore Object Oriented Programming (OOP) with classes and objects

Exceptions

• Handle runtime errors using Exceptions

Input and Output

• Implement programs that read and write files

Data Structures

• Use advanced data structures like generators and comprehensions to reduce boilerplate code

Parsing JSON

• Read and write JSON data

Debugging

• Debug Python programs using the Python debugger (pdb)

Introduction to Red Hat OpenShift AI

• Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.

Data Science Projects

• Organize code and configuration by using data science projects, workbenches, and data connections

Jupyter Notebooks

• Use Jupyter notebooks to execute and test code interactively