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Jarrod Parkes
and
Meghan Kane
Take Udacity's free Machine Learning for iOS course and learn how to use Apple's Core ML framework to build iOS apps with intelligent new features. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/1h2ead/udacity
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What's inside
Syllabus
Core ML enables developers to use trained machine learning models in their apps. Learn the basics of Core ML and how to incorporate MobileNet, an image classification model, into an app.
Apple's default models are only the starting point for on-device machine learning. With custom models, you can define your interface and build a truly custom experience.
Enjoy bonus content like an interview with Meghan Kane, the engineer who inspired this course, two blog posts from fellow students, and a challenge app!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners who are interested in incorporating machine learning into iOS apps
Taught by experts in the field of machine learning for iOS development
Provides hands-on experience with Core ML and image classification models
May require prior knowledge in programming and iOS development
Focuses on Apple's Core ML framework, which may limit applicability to other platforms
Save this course
Save Core ML: Machine Learning for iOS to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Core ML: Machine Learning for iOS with these
activities:
Refresh your programming skills
Show steps
Revisit the basics of Swift and iOS development to ensure you have a strong foundation for this course.
Browse courses on
Mobile Application Development
Show steps
-
Review your notes or textbooks on Swift basics.
-
Practice writing simple Swift programs.
-
Build a small iOS app to test your understanding.
Explore online tutorials and resources on Core ML
Show steps
Supplement your learning by exploring additional resources and tutorials to broaden your knowledge.
Show steps
-
Search for online tutorials and documentation on Core ML.
-
Follow step-by-step guides to build projects and learn new concepts.
-
Experiment with different tutorials and resources to find what works best for you.
Practice using Core ML's Vision framework
Show steps
Gain proficiency in using Core ML's Vision framework through hands-on exercises.
Show steps
-
Follow tutorials or documentation on using the Vision framework.
-
Experiment with different Vision APIs and parameters.
-
Build small projects or exercises to apply your understanding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a simple image classifier app
Show steps
Apply your understanding of Core ML by building a practical app that can classify images.
Show steps
-
Design the user interface and functionality of your app.
-
Integrate Core ML into your app and load a pre-trained image classification model.
-
Test and refine your app's accuracy.
Write a blog post about your experience learning Core ML
Show steps
Reflect on what you have learned and share your insights to reinforce your understanding and help others.
Show steps
-
Identify the key points and takeaways from your learning.
-
Outline your blog post and gather supporting materials.
-
Write and edit your blog post, explaining the concepts clearly and engagingly.
-
Publish your blog post and share it with others.
Develop a custom Core ML model for a specific image classification task
Show steps
Challenge yourself by creating a custom model that meets a specific need or improves upon existing models.
Show steps
-
Define the problem and gather a dataset of labeled images.
-
Train and evaluate different machine learning algorithms to find the best model.
-
Convert your model to the Core ML format and integrate it into an app.
-
Test and refine your model's performance.
Refresh your programming skills
Show steps
Revisit the basics of Swift and iOS development to ensure you have a strong foundation for this course.
Browse courses on
Mobile Application Development
Show steps
Show steps
Show steps
- Review your notes or textbooks on Swift basics.
- Practice writing simple Swift programs.
- Build a small iOS app to test your understanding.
Explore online tutorials and resources on Core ML
Show steps
Supplement your learning by exploring additional resources and tutorials to broaden your knowledge.
Show steps
Show steps
Show steps
- Search for online tutorials and documentation on Core ML.
- Follow step-by-step guides to build projects and learn new concepts.
- Experiment with different tutorials and resources to find what works best for you.
Practice using Core ML's Vision framework
Show steps
Gain proficiency in using Core ML's Vision framework through hands-on exercises.
Show steps
Show steps
Show steps
- Follow tutorials or documentation on using the Vision framework.
- Experiment with different Vision APIs and parameters.
- Build small projects or exercises to apply your understanding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a simple image classifier app
Show steps
Apply your understanding of Core ML by building a practical app that can classify images.
Show steps
Show steps
Show steps
- Design the user interface and functionality of your app.
- Integrate Core ML into your app and load a pre-trained image classification model.
- Test and refine your app's accuracy.
Write a blog post about your experience learning Core ML
Show steps
Reflect on what you have learned and share your insights to reinforce your understanding and help others.
Show steps
Show steps
Show steps
- Identify the key points and takeaways from your learning.
- Outline your blog post and gather supporting materials.
- Write and edit your blog post, explaining the concepts clearly and engagingly.
- Publish your blog post and share it with others.
Develop a custom Core ML model for a specific image classification task
Show steps
Challenge yourself by creating a custom model that meets a specific need or improves upon existing models.
Show steps
Show steps
Show steps
- Define the problem and gather a dataset of labeled images.
- Train and evaluate different machine learning algorithms to find the best model.
- Convert your model to the Core ML format and integrate it into an app.
- Test and refine your model's performance.
Career center
Learners who complete Core ML: Machine Learning for iOS will develop knowledge and skills
that may be useful to these careers:
Mobile Developer
Mobile Developer
Mobile Developers design, develop, and maintain mobile applications. Core ML: Machine Learning for iOS is essential for Mobile Developers wanting to build iOS apps with machine learning capabilities. The course teaches the basics of Core ML, Apple's framework for building machine learning apps. It also covers incorporating pre-built and custom models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, deploy, and maintain machine learning algorithms, leveraging knowledge of computer science, software engineering, and mathematics. Core ML: Machine Learning for iOS provides a strong foundation by teaching the fundamentals of Core ML, Apple's framework for building machine learning apps. The course discusses incorporating pre-built and custom models, giving you the knowledge to design, deploy, and maintain machine learning solutions on iOS.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop algorithms and systems to enable computers to see and interpret images. Core ML: Machine Learning for iOS is valuable for Computer Vision Engineers interested in developing iOS apps that incorporate computer vision. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can analyze images and make predictions.
Software Engineer
Software Engineer
Software Engineers design, build, deploy, and maintain software applications. Core ML: Machine Learning for iOS is valuable for Software Engineers interested in developing iOS apps with advanced features. The course teaches the basics of Core ML, Apple's framework for building machine learning apps. It also covers incorporating pre-built and custom models. This knowledge is essential for building mobile apps that incorporate machine learning.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret large sets of data to uncover patterns and insights. Core ML: Machine Learning for iOS may be useful for Data Scientists interested in using machine learning to create iOS apps. The course covers incorporating pre-built and custom models, giving you the knowledge to build data-driven iOS apps that provide insights and make predictions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data to make investment decisions. Core ML: Machine Learning for iOS may be useful for Quantitative Analysts interested in using machine learning to develop trading strategies. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can analyze financial data and make predictions.
UX Designer
UX Designer
UX Designers create user interfaces for websites and apps, making sure they are both usable and visually appealing. Core ML: Machine Learning for iOS may be useful for UX Designers interested in creating iOS apps that incorporate machine learning. The course covers incorporating pre-built and custom models, giving you the knowledge to build apps that are both user-friendly and intelligent.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. Core ML: Machine Learning for iOS may be useful for Robotics Engineers interested in developing iOS apps to control and interact with robots. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can implement and evaluate machine learning algorithms for robot control and interaction.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. Core ML: Machine Learning for iOS may be useful for Artificial Intelligence Engineers interested in developing iOS apps that incorporate machine learning. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can implement and evaluate artificial intelligence algorithms.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop algorithms and systems to enable computers to understand and process human language. Core ML: Machine Learning for iOS may be useful for Natural Language Processing Engineers interested in developing iOS apps that incorporate natural language processing. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can analyze text and make predictions.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. Core ML: Machine Learning for iOS may be useful for Machine Learning Researchers interested in developing iOS apps to test and deploy their algorithms. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can implement and evaluate new machine learning algorithms.
Business Analyst
Business Analyst
Business Analysts assess an organization's operations and suggest ways to improve efficiency and profitability. Core ML: Machine Learning for iOS may be useful for Business Analysts interested in using machine learning to improve business processes. The course covers incorporating pre-built and custom models, giving you the knowledge to build mobile apps that provide insights and make predictions.
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data to help organizations make informed decisions. Core ML: Machine Learning for iOS may be useful for Data Analysts interested in using machine learning to create interactive dashboards and reports. The course covers incorporating pre-built and custom models, giving you the knowledge to build data-driven iOS apps that provide insights and make predictions.
Product Manager
Product Manager
Product Managers define the vision and roadmap for a product, working with engineers and designers to bring it to life. Core ML: Machine Learning for iOS may be useful for Product Managers interested in developing innovative iOS apps that incorporate machine learning. The course teaches you how to think about machine learning in the context of product development, and how to incorporate it into your roadmap.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. Core ML: Machine Learning for iOS may be useful for Data Engineers interested in using iOS apps to monitor and manage data pipelines. The course covers incorporating pre-built and custom models, giving you the knowledge to build iOS apps that can analyze data and make predictions.
For more career information including salaries, visit:
OpenCourser.com/course/1h2ead/udacity
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Core ML: Machine Learning for iOS.
Provides a hands-on guide to deep learning, covering a variety of topics from building simple neural networks to training complex models. It valuable resource for both beginners and experienced practitioners who want to gain a practical understanding of deep learning.
Provides a comprehensive introduction to machine learning using Python, the popular programming language. It covers a wide range of topics from data preprocessing to model evaluation.
Provides a practical guide to machine learning, using Scikit-Learn and TensorFlow. It covers a wide range of topics from data preprocessing to model evaluation.
Provides a comprehensive introduction to machine learning using Python, the popular programming language. It covers a wide range of topics from data preprocessing to model evaluation.
Provides a comprehensive overview of machine learning algorithms, covering a wide range of topics from linear regression to deep learning. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of machine learning.
Provides a collection of recipes for solving machine learning problems using Python. It covers a wide range of topics from data preprocessing to model evaluation.
Provides a comprehensive introduction to machine learning using R, the popular statistical programming language. It covers a wide range of topics from data preprocessing to model evaluation.
Provides a visual introduction to deep learning, using clear and concise illustrations to explain the fundamental concepts and techniques used in the field. It valuable resource for both beginners and experienced practitioners who want to gain a deeper understanding of deep learning.
Provides a comprehensive introduction to machine learning, covering a wide range of topics from data preprocessing to model evaluation. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
Provides a practical guide to machine learning for hackers, covering a wide range of topics from data preprocessing to model evaluation. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
Provides a comprehensive introduction to deep learning, covering the fundamental concepts and techniques used in the field. It valuable resource for both beginners and experienced practitioners who want to gain a deep understanding of deep learning.
Provides a gentle introduction to machine learning, covering the fundamental concepts and techniques used in the field. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1h2ead/udacity
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Effort
1 hour
Via
Udacity
Instructors
Jarrod Parkes
Meghan Kane
Language
English
Good to know
Suitable for beginners who are interested in incorporating machine learning into iOS apps
Taught by experts in the field of machine learning for iOS development
Provides hands-on experience with Core ML and image classification models
May require prior knowledge in programming and iOS development
Focuses on Apple's Core ML framework, which may limit applicability to other platforms
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