Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)
- Course Duration
- 5 Days
Need Group Training
Course Description
An introduction to Python programming, to machine learning concepts, and how to use Red Hat OpenShift AI to train ML models.
Python is a popular programming language used by system administrators, data scientists, and developers to create applications, perform statistical analysis, and train AI/ML models. This course introduces the Python language and teaches students basic machine learning concepts, and the different types of machine learning. This course helps students build core skills such as using Red Hat OpenShift AI to train ML models and how to apply best practices when training models through hands-on experience.
This course is based on Python 3, RHEL 9.0, Red Hat OpenShift ® 4.14, and Red Hat OpenShift AI 2.8.
Course Objective
- 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
- Use powerful regular expressions in Python to manipulate text
- How to effectively structure large Python programs using modules and namespaces
- Introduction to Machine Learning
- Training Models
- Enhancing Model Training with RHOAI
Course Outline
1 – Basic Python Syntax
- Explore the basic syntax and semantics of Python
2 – Language Components
- Understand the basic control flow features and operators
3 – Collections
- Write programs that manipulate compound data using lists, sets, tuples and dictionaries
4 – Functions
- Decompose your programs into composable functions
5 – Modules
- Organize your code using Modules for flexibility and reuse
6 – Classes in Python
- Explore Object Oriented Programming (OOP) with classes and objects
7 – Exceptions
- Handle runtime errors using Exceptions
8 – Input and Output
- Implement programs that read and write files
9 – Data Structures
- Use advanced data structures like generators and comprehensions to reduce boilerplate code
10 – Parsing JSON
- Read and write JSON data
11 – Debugging
- Debug Python programs using the Python debugger (pdb)
12 – Introduction to Machine Learning
- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
13 – Training Models
- Train models by using default and custom workbenches
14 – Enhancing Model Training with RHOAI
- Use RHOAI to apply best practices in machine learning and data science