
Here is an overview of this new section.
In this lecture, you will learn how to download and install xcode for iOS 11
In this lecture, you will learn how to get the Apple developer license for iOS
In this lecture, you will learn how to use a MAC computer on Windows PC and on Linux
In this lecture, you will learn how to install the iOS 11 on your iPhone or iPad
In this lecture, you will learn how to use Xcode interface
In this lecture, you will learn how to work with xcode configuration files
Here is an overview of this new section.
In this lecture, you will get a quick and simple explanation of what an artificial neuron is and how they form a neural network
In this lecture, you will get a quick and simple explanation of how an Artificial Neural Network functions
In this lecture, you will get a quick and simple explanation of what how a Convolution Neural Network functions
In this lecture, you will get a quick and simple explanation of what parts make up a Recurrent Neural Network
Here is an overview of this new section.
Please download the Cheat Sheet PDF file.
In this lecture, we will give a quick demonstration of what we're trying to achieve in this section.
In this lecture, you will learn about machine learning models and neural networks.
In this lecture, you will learn how to create an xcode project that uses Swift
In this lecture, you will learn how to add ML models into XCode projects
In this lecture, you will learn how to get pre-made, iOS ready, machine learning models
In this lecture, you will learn how to use ML models that take images as input (part 1 of 2)
In this lecture, you will learn how to use ML models that take images as input (part 2 of 2)
In this lecture, you will learn how to program a Vision Request callback method to process the trained model prediction results.
Note: Vision framework always returns the best prediction as the first element of the results array.
In this lecture, you will get to test different ML models.
Here is an exercise to make sure you've grasped what we've learned so far
Solution of the previous lecture's exercise.
Here is a summary of what we've learned so far, and how to go in depth
Here is an overview of this new section.
In this lecture, you will learn how to find custom ML models that are not served by Apple
In this lecture, you will learn how to get the development environment named Anaconda, which is an amazing tool for AI scientists
In this lecture, you will learn how to install the python packages (libraries) to work with CoreML
In this lecture, you will learn how to install Caffee tools to convert Caffe model types to .mlmodel ones.
In this lecture, you will learn how to convert scikit models to coreML mlmodel format
Here is an overview of this new section.
In this lecture, you will learn create a new xcode project and add the custom iris.mlmodel we created in previous lectures.
In this lecture, you will learn how to build the project's User Interface
In this lecture, you will learn how to work with properties and the uipicker delegate methods
In this lecture, you will learn how to program the pickerview data source methods
In this lecture, you will learn how to code model prediction for data sets
Here, we'll make some code improvements
We'll go over import informations on data set models.
In this lecture, you will learn how to create the xcode project and add the fat VGG16 model to our project.
Let's build the project's user interface
Let's setup variables to capture the camera video stream.
In this lecture, you will learn how to program the camera feed.
In this lecture, you will learn how to capture images from the video stream so that we can analyze them with an mlmodel prediction.
In this lecture, you will learn how to program the ML prediction launch
In this lecture, you will learn how to process the ML model results.
We finally get to test all our hard work:
Note: R2D2 definitely looks like a trash can and Yoda like a green hot baked potato, don't you think?!
In this lecture, you will learn how to add converted model's metadata
In this lecture, you will learn how to get a pixelbuffer from a UIImage
In this lecture, you will learn how to extend a uiimage to return a pixel buffer (part 1 of 2)
In this lecture, you will learn how to extend a uiimage to return a pixel buffer (part 2 of 2)
In this lecture, you will learn how to use the UIImage pixel buffer for predictions
In this section, we will go over converting trained models from several python packages into .MLMODEL format to use them with iOS.
In this lecture, you will learn how to get a Caffe model, weights and labels
In this lecture, you will learn how to code the conversion of a Caffe model using its trained data, weights, labels, scale and rgb means.
In this lecture, you will learn how to export the caffemodel to mlmodel format.
In this lecture, you will finally get to see the converted Caffe model used in the iOS app. Isn't it amazing?
In this lecture, you will learn how to find Keras models, open them in a Python editor, and run the code.
You will also learn how to save and retrieve the data models and finally get to convert them to .MLMODEL format to be able to use them in iOS apps.
In this lecture, you will learn how to use different type of inputs for the VNImageRequestHandler
If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.
In this course, you will:
Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
Develop an intuitive sense for using Machine Learning in your iOS apps
Create 7 projects from scratch in practical code-along tutorials
Find pre-trained ML models and make them ready to use in your iOS apps
Create your own custom models
Add Image Recognition capability to your apps
Integrate Live Video Camera Stream Object Recognition to your apps
Add Siri Voice speaking feature to your apps
Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
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And more!
This course is also full of practical use cases and real-world challenges that allow you to practice what you’re learning. Are you tired of courses based on boring, over-used examples? Yes? Well then, you’re in a treat. We’ll tackle 5 real-world projects in this course so you can master topics such as image recognition, object recognition, and modifying existing trained ML models. You’ll also create an app that classifies flowers and another fun project inspired by Silicon Valley™ Jian Yang’s masterpiece: a Not-Hot Dog classifier app!
Why Machine Learning on iOS
One of the hottest growing fields in technology today, Machine Learning is an excellent skill to boost your your career prospects and expand your professional tool kit. Many of Silicon Valley’s hottest companies are working to make Machine Learning an essential part of our daily lives. Self-driving cars are just around the corner with millions of miles of successful training. IBM’s Watson can diagnose patients more effectively than highly-trained physicians. AlphaGo, Google DeepMind’s computer, can beat the world master of the game Go, a game where it was thought only human intuition could excel.
In 2017, Apple has made Machine Learning available in iOS so that anyone can build smart apps and games for iPhones, iPads, Apple Watches and Apple TVs. Nowadays, apps and games that do not have an ML layer will not be appealing to users. Whether you wish to change careers or create a second stream of income, Machine Learning is a highly lucrative skill that can give you an amazing sense of gratification when you can apply it to your mobile apps and games.
Why This Course Is Different
Machine Learning is very broad and complex; to navigate this maze, you need a clear and global vision of the field. Too many tutorials just bombard you with the theory, math, and coding. In this course, each section focuses on distinct use cases and real projects so that your learning experience is best structured for mastery.
This course brings my teaching experience and technical know-how to you. I’ve taught programming for over 10 years, and I’m also a veteran iOS developer with hands-on experience making top-ranked apps. For each project, we will write up the code line by line to create it from scratch. This way you can follow along and understand exactly what each line means and how to code comes together. Once you go through the hands-on coding exercises, you will see for yourself how much of a game-changing experience this course is.
As an educator, I also want you to succeed. I’ve put together a team of professionals to help you master the material. Whenever you ask a question, you will get a response from my team within 48 hours. No matter how complex your question, we will be there–because we feel a personal responsibility in being fully committed to our students.
By the end of the course, you will confidently understand the tools and techniques of Machine Learning for iOS on an instinctive level.
Don’t be the one to get left behind. Get started today and join millions of people taking part in the Machine Learning revolution.
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