What are the prerequisites for Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)?

Experience with Git is required Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building Kubernetes Applications (DO288) course Basic experience in the AI, data science, and machine learning fields is recommended

What happens if I need to cancel Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)?

Course fees are fully refundable with written notice at least ten business days before the class. Inside that window the fee is forfeited, though one free retake is available within six months of the class date.

Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)

Q: How much does Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253) cost?

$4,400 per seat. That covers courseware and lab access. Exam vouchers are quoted separately. Volume pricing is available for teams, and a private cohort is usually cheaper per seat past about four people.

Q: How long is Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)?

5 days of instructor-led training, delivered live online. Classes run during business hours in the timezone shown on each date.

Q: When is the next Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253) class?

This course currently runs as a private cohort rather than on the public schedule. Tell us your dates and headcount and we will schedule it.

Q: Is Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253) delivered online?

Yes. It runs live online with an instructor, not as a recording, so you can ask questions as you go. It can also be delivered privately at your site.

Q: Can a government agency buy Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)?

Yes. Purchase orders and government purchase cards are both accepted at checkout, and quotes can be formatted for a solicitation response. Terms are net 30 on invoiced orders.

Upcoming dates

No public dates scheduled right now

This class runs as a private cohort on dates that suit your team, at your site or delivered live online. Tell us roughly when and how many people, and we'll come back with dates and a total.

Private delivery is usually faster and works out better per seat once you're past four or five people.

Request private dates

Or leave your details and we will email you the moment a public date is scheduled.

Email Name Seats Roughly when

One email when a date is set. Nothing else, and no list.

About this class

Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253) is not on the public schedule at the moment, and runs as a private cohort on dates you choose. Seats are $4,400 each over 5 days, courseware included. Tell us your dates and headcount and a quote comes back the same business day. Purchase orders welcome.

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.

What you'll be able to do

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

Before you attend

Experience with Git is required
Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building Kubernetes Applications (DO288) course
Basic experience in the AI, data science, and machine learning fields is recommended

Who this is for

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