AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI Course Overview

Prerequisites

To ensure a successful learning experience in the AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI course, we recommend that students meet the following minimum requirements:

• Experience with Git: Basic understanding and hands-on experience with Git version control is essential.
• Experience in Red Hat OpenShift: Familiarity with Red Hat OpenShift, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course, is required.
• Basic experience in AI, Data Science, and Machine Learning: A foundational understanding or prior experience in AI, data science, and machine learning concepts is recommended to grasp the course content more effectively.

Target Audience for AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI

1. A brief introduction about the course and its relevant target audience:
   AI253 is a comprehensive course designed for professionals to master Python programming and leveraging Red Hat OpenShift AI for machine learning model training.

   
   

2. Job roles and audience for the course in a bullet point format:

   
   

• Data Scientists
• Machine Learning Engineers
• System Administrators
• Python Developers
• Cloud Engineers
• AI/ML Enthusiasts
• Software Developers
• DevOps Engineers
• Data Analysts
• IT Professionals looking to specialize in AI/ML
• Red Hat OpenShift Users
• Technical Leads in AI/ML Projects
• Research Scientists in the field of Machine Learning
• Technical Managers overseeing AI/ML initiatives
• Hobbyists aiming to advance their skills in Python and ML with OpenShift

Learning Objectives - What you will Learn in this AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI?

Course Overview

The AI253 course, "Creating Machine Learning Models with Python and Red Hat OpenShift AI," introduces students to Python programming, machine learning concepts, and how to effectively use Red Hat OpenShift AI to train ML models, covering both theoretical knowledge and hands-on practice.

Learning Objectives and Outcomes

• Basic Python Syntax: Understand the foundational syntax and semantics of Python.
• Language Components: Learn the basic control flow features and operators.
• Collection Manipulation: Write programs manipulating lists, sets, tuples, and dictionaries.
• Functions: Decompose your programs into reusable, composable functions.
• Modules: Organize code using modules for enhanced flexibility and reuse.
• Object-Oriented Programming: Explore Object-Oriented Programming with classes and objects.
• Error Handling: Handle runtime errors with Exceptions.
• File I/O: Implement programs that perform reading and writing of files.
• Advanced Data Structures: Utilize advanced data structures like generators and comprehensions.
• JSON Parsing: Read and write JSON data.
• Debugging Skills: Debug Python programs using the Python debugger (pdb).
• Introduction to Machine Learning: Grasp basic machine learning concepts, types,