Machine Learning with Core ML 2 and Swift 5 - A Beginner-Friendly Guide

Karoly Nyisztor, Senior Software Engineer, Instructor

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43 Lessons (2h 9m)
    • 1. Machine Learning with Core ML 2 and Swift

      1:30
    • 2. 1.1 Introduction - Prerequisites

      1:35
    • 3. 1.2 What is Machine Learning?

      2:27
    • 4. 1.3 Supervised and Unsupervised Machine Learning

      2:00
    • 5. 1.4 The Machine Learning Model

      2:43
    • 6. 1.5 iOS and Machine Learning

      1:05
    • 7. 1.6 Exercise Files

      1:00
    • 8. 2.1 iOS Machine Learning Architecture

      1:15
    • 9. 2.2 The Core ML Framework

      1:13
    • 10. 2.3 Natural Language Processing

      1:25
    • 11. 2.4 The Vision Framework

      1:47
    • 12. 2.5 The GamePlayKit Framework

      3:08
    • 13. 3.1 Natural Language Text Analysis

      1:23
    • 14. 3.2 Recognizing the Dominant Language of a Text

      6:06
    • 15. 3.3 Enumerating the Words in a Text

      4:13
    • 16. 3.4 Identifying Parts of Speech

      3:22
    • 17. 3.5 Identifying People, Places and Organizations

      2:16
    • 18. 4.1 Image Analysis with the Vision Framework

      0:52
    • 19. 4.2 The Starter App

      3:45
    • 20. 4.3 Analyzing Still Images using Vision

      1:30
    • 21. 4.4 Implementing the Image Request Handler

      7:05
    • 22. 4.5 Detecting Rectangular Areas - The Image Analysis Request

      6:30
    • 23. 4.6 Converting Coordinates Between Quartz 2D and UIKit

      3:55
    • 24. 4.7 Visualizing the Detected Rectangles

      6:35
    • 25. 4.8 Recognizing Text, Faces and Barcodes in Still Images

      4:25
    • 26. 5.1 Training a Flower Classifier on Your Computer using Create ML

      0:42
    • 27. 5.2 Recognizing Flowers - Preparing the Training Data

      2:24
    • 28. 5.3 Training an Image Classifier in a Playground

      4:31
    • 29. 5.4 Recognizing Flowers - the Starter App

      2:02
    • 30. 5.5 Integrating the Flower Classifier Model

      4:00
    • 31. 5.6 Displaying Predictions

      3:00
    • 32. 5.7 Picking an Image

      2:37
    • 33. 5.8 Performing the Image Analysis Request

      3:00
    • 34. 5.9 The Flower Recognizer App in Action

      2:44
    • 35. 6.1 Determining the Tonality of a Review

      0:36
    • 36. 6.2 Preparing the Training Data

      1:23
    • 37. 6.3 Training a Text Classifier in a Playground

      6:03
    • 38. 6.4 Creating the MLTextClassifier

      5:16
    • 39. 6.5 Saving the Core ML Model

      2:57
    • 40. 6.6 Laying Out the User Interface of the Review Classifier App

      2:54
    • 41. 6.7 Integrating the Review Classifier Model

      6:42
    • 42. 6.8 Testing the Review Classifier App

      4:25
    • 43. Goodbye!

      1:03

Project Description

Practice makes the master. Try to put the techniques described in this course into practice.

Create an app that identifies people in a photo and tells their gender. You can use datasets that are freely available on kaggle.com, and similar sites.

Student Projects