• views: 86
• date: 30 March 2026
• posted by: voska89

What you'll learn

Gain insights from a software engineer with over 30 years of hands-on professional experience.
Get a practical introduction to machine learning in iOS and macOS development.
Gain a practical understanding of Create ML, Core ML, Vision, and Natural Language Processing.
Train and evaluate custom image classifiers directly on your Mac using Create ML.
Set up and integrate Core ML models into SwiftUI projects.
Build iOS apps that can recognize objects, faces, barcodes, and text in photos and video streams.
Integrate natural language text analysis and sentiment detection into your apps.
The companion eBook (a $28.80 value on Amazon) is included free!

Requirements

To follow along, you'll need a Mac running macOS Tahoe and Xcode 26, Apple's latest development environment.
You should be comfortable with the basics of Swift 5 or Swift 6 programming — just enough to read and write simple code.
You should be familiar with Xcode basics such as creating projects and running apps.

Description

A practical, concise, and hands-on machine learning course you can complete in just a few hours — with a companion eBook included.
Wouldn't it be great to add intelligent features like image recognition, natural language processing, or sentiment analysis to your iOS, macOS, or iPadOS apps?
In this course, you'll learn how to unleash the power of machine learning using Core ML, Create ML, Vision, and Swift 6 with SwiftUI.
We'll start by demystifying what machine learning is and how it works — explained in plain English, without jargon. We'll explore Apple's machine learning frameworks through real examples and hands-on Swift coding.
You'll build practical iOS apps that can
• Recognize dog breeds from photos
• Analyze the sentiment of product reviews
• Detect faces, barcodes, and text in images using Vision
You'll also learn how to train your own machine learning models right on your Mac using Create ML.
And there's a lot more packed into this professional, focused course.
About the Instructor
I've been designing and building software for over 30 years for companies like Apple, Siemens, and SAP.
As a software architect, I helped create enterprise frameworks, including Siemens Healthcare's syngo image processing system and Apple–SAP's Cloud Platform SDK for iOS.
I currently hold twelve patents in the field of mobile computing.
What You'll Learn
• How Apple's machine learning frameworks fit together (Core ML, Create ML, Vision, NaturalLanguage)
• Natural language text processing and sentiment analysis
• Setting up and integrating Core ML models in Xcode projects
• Image recognition and object detection with the Vision framework
• Training and testing your own image classifiers on your Mac
Student Reviews
"Thank you Karoly, you've delivered another excellent course with detailed explanations and real-world examples that any app developer can put into practice."
— Jim McMillan
"The best introduction to Machine Learning with Swift — clear, practical, and beginner-friendly."
— Zbyszek Pietras
"Finally, a course that covers Core ML, natural language processing, and Create ML — exactly what I was looking for."
— Dan Gray
More Than an Online Course
• Personalized support: access to a private forum where I personally answer student questions
• Companion eBook: included with the course
• able demo projects: follow along and experiment with working examples
• Continuous updates: I keep the content current with the latest Apple tools and frameworks
30-Day Money-Back Guarantee
If you're not completely satisfied, you'll receive a full refund — no questions asked.
Go ahead and click Enroll. See you in the first lesson!

Who this course is for

Take this course to start building intelligent iOS apps with Create ML, Core ML, Vision, and Natural Language Processing.
This course is for you if you want to build smarter, more capable apps powered by machine learning.
Enroll if you're interested in exploring the latest Apple technologies in the field of artificial intelligence.




   
   
   

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