Complete iOS Machine Learning Masterclass
Complete iOS Machine Learning Masterclass, available at $59.99, has an average rating of 4.7, with 97 lectures, 3 quizzes, based on 301 reviews, and has 16828 subscribers.
You will learn about Build smart iOS 11 & Swift 4 apps using Machine Learning Use trained ML models in your apps Convert ML models to iOS ready models Create your own ML models Apply Object Prediction on pictures, videos, speech and text Discover when and how to apply a smart sense to your apps This course is ideal for individuals who are People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence or People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert or Developers who would like to apply their Machine Learning skills by creating practical mobile apps or Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements It is particularly useful for People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence or People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert or Developers who would like to apply their Machine Learning skills by creating practical mobile apps or Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements.
Enroll now: Complete iOS Machine Learning Masterclass
Summary
Title: Complete iOS Machine Learning Masterclass
Price: $59.99
Average Rating: 4.7
Number of Lectures: 97
Number of Quizzes: 3
Number of Published Lectures: 97
Number of Published Quizzes: 3
Number of Curriculum Items: 100
Number of Published Curriculum Objects: 100
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Build smart iOS 11 & Swift 4 apps using Machine Learning
- Use trained ML models in your apps
- Convert ML models to iOS ready models
- Create your own ML models
- Apply Object Prediction on pictures, videos, speech and text
- Discover when and how to apply a smart sense to your apps
Who Should Attend
- People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence
- People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert
- Developers who would like to apply their Machine Learning skills by creating practical mobile apps
- Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements
Target Audiences
- People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence
- People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert
- Developers who would like to apply their Machine Learning skills by creating practical mobile apps
- Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements
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:
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Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
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Develop an intuitive sense for using Machine Learning in your iOS apps
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Create 7 projects from scratch in practical code-along tutorials
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Find pre-trained ML models and make them ready to use in your iOS apps
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Create your own custom models
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Add Image Recognition capability to your apps
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Integrate Live Video Camera Stream Object Recognition to your apps
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Add Siri Voice speaking feature to your apps
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Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
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Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
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Get FREE unlimited hosting for one year
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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.
topics: ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection
Course Curriculum
Chapter 1: Getting started
Lecture 1: About Your Instructor and Course Overview
Lecture 2: About Machine Learning
Lecture 3: Activity: Playing with Machine Learning Style Transfer
Chapter 2: Optional – iOS Fundamentals
Lecture 1: About this section – start iOS
Lecture 2: Download and install xcode for iOS 11
Lecture 3: Get the iOS developer license
Lecture 4: How to use a MAC on Windows PC or Linux
Lecture 5: How to install iOS 11 on your iPhone or iPad
Lecture 6: Use the Xcode interface
Lecture 7: Xcode configuration files
Chapter 3: Optional – Machine Learning Concepts
Lecture 1: About this section – intro to ML
Lecture 2: What is an Artificial Neuron – Neural Network
Lecture 3: Parts of an Artificial Neural Network
Lecture 4: Explanation – Convolutional Neural Network
Lecture 5: Recurrent Neural Networks basics RNNs
Chapter 4: iOS Machine Learning With Photos
Lecture 1: About this section – coreML with Photos
Lecture 2: Demo of project using coreML on photos
Lecture 3: About ML model and Neural Networks
Lecture 4: Project: Create the xcode project
Lecture 5: Project: How to add ML models to xcode projects
Lecture 6: Project: How to get pre-made ML models for iOS
Lecture 7: Project: How to use ML models with images (part 1)
Lecture 8: Project: How to use ML models with images (part 2)
Lecture 9: Project: Programming the VN request callback method
Lecture 10: Testing different ML models
Lecture 11: Exercise: Models with Images input
Lecture 12: Solution: Models with Images input
Lecture 13: Summary: coreML Vision with Photos
Chapter 5: coreML All about custom models
Lecture 1: About this section – model conversion
Lecture 2: Project: Finding custom ML models
Lecture 3: Project: Converting ML models get Anaconda IDE
Lecture 4: Installing Python libraries for core ML
Lecture 5: Installing Caffe tools for core ML conversion
Lecture 6: Project: Converting scikit model to core ml mlmodel format
Chapter 6: CoreML with Data Set models
Lecture 1: Introduction to Working with Data sets
Lecture 2: Project: Create xcode project and add iris model
Lecture 3: Project: ML dataset project User Interface
Lecture 4: Project: Properties and picker delegate methods
Lecture 5: Project: Pickerview data source methods
Lecture 6: Project: Coding prediction for data sets
Lecture 7: Project: Code improvements
Lecture 8: Important data set models information
Chapter 7: Project: coreML with Video Camera
Lecture 1: About CoreML with Video Camera
Lecture 2: Project: Create xcode project and add VGG16 model
Lecture 3: Project: Building the user interface
Lecture 4: Project: Video Stream variables setup
Lecture 5: Project: Program camera feed
Lecture 6: Project: Capture image from video stream for ML model
Lecture 7: Project: Programming the ML prediction launch
Lecture 8: Project: Processing the ML model output
Lecture 9: Testing the live camera feed with VGG model
Chapter 8: END: iOS coreML fundamentals
Lecture 1: Congratulations
Chapter 9: Optional – Going the extra mile
Lecture 1: Adding converted model metadata
Lecture 2: Get a PixelBuffer from a UIImage
Lecture 3: UIImage PixelBuffer extension (part 1)
Lecture 4: UIImage PixelBuffer extension (part 2)
Lecture 5: coreML prediction using UIImage PixelBuffer
Chapter 10: Optional – Numerous Model Conversions
Lecture 1: About model conversion types
Lecture 2: Caffe – Get a Caffe ML model with weights and labels
Lecture 3: CoreML tools conversion code with Caffe
Lecture 4: Exporting Caffe model to mlmodel format
Lecture 5: Caffe – Using the Caffe model with iOS
Lecture 6: Keras – Load Save Keras models and convert to mlmodel
Lecture 7: Vision Image Request parameter options
Chapter 11: Advanced Vision Techniques: Face Detection
Lecture 1: Introduction to advanced ML with Vision
Lecture 2: Project: Create the user interface in storyboard
Lecture 3: Project: Coding the Photo selection
Lecture 4: Project: Coding Face Detection
Lecture 5: Activity: Face Detection
Lecture 6: Activity Solution: Face Detection
Chapter 12: Optional – Advanced Face Features Detection
Lecture 1: About Advanced Face Feature Recognition
Lecture 2: Locate face position and area (part 1 of 2)
Lecture 3: Locate face position and area (part 2 of 2)
Lecture 4: Code to detect Face features eyes nose lips
Lecture 5: face features part 2
Lecture 6: face features part 3
Lecture 7: face features part 4
Lecture 8: Activity – draw all face features in blue
Lecture 9: Activity Solution
Chapter 13: Advanced Text Detection Techniques
Lecture 1: About Project: Text Detection
Lecture 2: Project: text recog part 1
Lecture 3: Project: text recog part 2
Lecture 4: Project: text recog part 3
Lecture 5: Activity: Text Recognition
Instructors
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Yohann Taieb
Apps Games Unity iOS Android Apple Watch TV Development
Rating Distribution
- 1 stars: 7 votes
- 2 stars: 19 votes
- 3 stars: 44 votes
- 4 stars: 77 votes
- 5 stars: 154 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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