Complete iOS 11 Machine Learning Masterclass
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Complete iOS 11 Machine Learning Masterclass

iOS 11 & Swift 4 the most comprehensive course on Machine Learning for iOS development. Master building smart apps iOS11
4.6 (67 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,371 students enrolled
Created by Yohann Taieb
Last updated 8/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 7.5 hours on-demand video
  • 3 Articles
  • 23 Supplemental Resources
  • 1 Practice Test
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • Basic understanding of programming
  • Have access to a MAC computer or MACinCloud website
Description

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

  • Get FREE unlimited hosting for one year

  • 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 11 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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Who is the target audience?
  • 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
Compare to Other iOS Development Courses
Curriculum For This Course
97 Lectures
07:34:28
+
Getting started
3 Lectures 10:57

About Machine Learning
04:29

Activity: Playing with Machine Learning Style Transfer
02:14
+
Optional - iOS Fundamentals
7 Lectures 22:38

Here is an overview of this new section.

Preview 00:45

In this lecture, you will learn how to  download and install xcode for iOS 11

Preview 02:32

In this lecture, you will learn how to get the Apple developer license for iOS

Preview 02:18

In this lecture, you will learn how to use a MAC computer on Windows PC and on Linux

Preview 02:14

In this lecture, you will learn how to install the iOS 11 on your iPhone or iPad

Preview 02:51

In this lecture, you will learn how to use Xcode interface

Preview 06:20

In this lecture, you will learn how to work with xcode configuration files

Preview 05:38
+
Optional - Machine Learning Concepts
5 Lectures 16:25

Here is an overview of this new section.

About this section - intro to ML
01:25

In this lecture, you will get a quick and simple explanation of what an artificial neuron is and how they form a neural network

Preview 04:31

In this lecture, you will get a quick and simple explanation of how an Artificial Neural Network functions

Parts of an Artificial Neural Network
04:16

In this lecture, you will get a quick and simple explanation of what how a Convolution Neural Network functions

Explanation - Convolutional Neural Network
03:28

In this lecture, you will get a quick and simple explanation of what parts make up a Recurrent Neural Network

Recurrent Neural Networks basics RNNs
02:45
+
iOS Machine Learning With Photos
13 Lectures 59:05

Here is an overview of this new section.

Please download the Cheat Sheet PDF file.

Preview 00:49

In this lecture, we will give a quick demonstration of what we're trying to achieve in this section.

Preview 01:35

In this lecture, you will learn about machine learning models and neural networks.

About ML model and Neural Networks
02:39

In this lecture, you will learn how to create an xcode project that uses Swift

Project: Create the xcode project
04:30

In this lecture, you will learn how to add ML models into XCode projects

Project: How to add ML models to xcode projects
07:49

In this lecture, you will learn how to get pre-made, iOS ready, machine learning models

Project: How to get pre-made ML models for iOS
06:15

In this lecture, you will learn how to use ML models that take images as input (part 1 of 2)

Project: How to use ML models with images (part 1)
09:39

In this lecture, you will learn how to use ML models that take images as input (part 2 of 2)

Project: How to use ML models with images (part 2)
08:15

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.

Project: Programming the VN request callback method
05:23

In this lecture, you will get to test different ML models.

Testing different ML models
06:03

Here is an exercise to make sure you've grasped what we've learned so far

Preview 00:55

Solution of the previous lecture's exercise.

Solution: Models with Images input
04:05

Test your knowledge on the basic of Machine Learning for iOS

Basics of Machine Learning for iOS
4 questions

Here is a summary of what we've learned so far, and how to go in depth

Summary: coreML Vision with Photos
01:08
+
coreML All about custom models
6 Lectures 40:16

Here is an overview of this new section.

Preview 01:15

In this lecture, you will learn how to find custom ML models that are not served by Apple

Project: Finding custom ML models
04:17

In this lecture, you will learn how to get the development environment named Anaconda, which is an amazing tool for AI scientists

Project: Converting ML models get Anaconda IDE
04:53

In this lecture, you will learn how to install the python packages (libraries) to work with CoreML

Installing Python libraries for core ML
05:23

In this lecture, you will learn how to install Caffee tools to convert Caffe model types to  .mlmodel ones.

Installing Caffe tools for core ML conversion
11:01

In this lecture, you will learn how to convert scikit models to coreML mlmodel format

Project: Converting scikit model to core ml mlmodel format
13:27

Questions revolving around using custom ML model in your apps

Working with Custom Models
3 questions
+
CoreML with Data Set models
8 Lectures 44:52

Here is an overview of this new section.

Introduction to Working with Data sets
01:49

In this lecture, you will learn create a new xcode project and add the custom iris.mlmodel we created in previous lectures.

Project: Create xcode project and add iris model
01:10

In this lecture, you will learn how to build the project's User Interface

Project: ML dataset project User Interface
10:39

In this lecture, you will learn how to work with properties and the uipicker delegate methods

Project: Properties and picker delegate methods
08:54

In this lecture, you will learn how to program the pickerview data source methods

Project: Pickerview data source methods
03:38

In this lecture, you will learn how to code model prediction for data sets

Project: Coding prediction for data sets
08:16

Here, we'll make some code improvements

Project: Code improvements
06:51

We'll go over import informations on data set models.

Important data set models information
03:35

The following questions are meant to verify you've got the most out of how to work with ML data sets.

Working with Data Sets
3 questions
+
Project: coreML with Video Camera
9 Lectures 40:31
About CoreML with Video Camera
01:10

In this lecture, you will learn how to create the xcode project and add the fat VGG16 model to our project.

Preview 01:40

Let's build the project's user interface

Project: Building the user interface
04:17

Let's setup variables to capture the camera video stream.

Project: Video Stream variables setup
05:11

In this lecture, you will learn how to program the camera feed.

Project: Program camera feed
06:24

In this lecture, you will learn how to capture images from the video stream so that we can analyze them with an mlmodel prediction.

Project: Capture image from video stream for ML model
07:43

In this lecture, you will learn how to program the ML prediction launch

Project: Programming the ML prediction launch
06:41

In this lecture, you will learn how to process the ML model results.

Project: Processing the ML model output
04:54

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?!

Preview 02:31
+
END: iOS coreML fundamentals
1 Lecture 00:48
Congratulations
00:48
+
Optional - Going the extra mile
5 Lectures 28:54

In this lecture, you will learn how to add converted model's metadata

Adding converted model metadata
05:59

In this lecture, you will learn how to get a pixelbuffer from a UIImage

Get a PixelBuffer from a UIImage
02:57

In this lecture, you will learn how to extend a uiimage to return a pixel buffer (part 1 of 2)

UIImage PixelBuffer extension (part 1)
08:25

In this lecture, you will learn how to extend a uiimage to return a pixel buffer (part 2 of 2)

UIImage PixelBuffer extension (part 2)
07:45

In this lecture, you will learn how to use the UIImage pixel buffer for predictions

coreML prediction using UIImage PixelBuffer
03:48
+
Optional - Numerous Model Conversions
7 Lectures 36:48

In this section, we will go over converting trained models from several python packages into .MLMODEL format to use them with iOS.

About model conversion types
00:10

In this lecture, you will learn how to get a Caffe model, weights and labels

Caffe - Get a Caffe ML model with weights and labels
03:19

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.

CoreML tools conversion code with Caffe
09:04

In this lecture, you will learn how to export the caffemodel to mlmodel format.

Exporting Caffe model to mlmodel format
04:52

In this lecture, you will finally get to see the converted Caffe model used in  the iOS app. Isn't it amazing?

Preview 02:48

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.

Keras - Load Save Keras models and convert to mlmodel
08:58

In this lecture, you will learn how to use different type of inputs for the VNImageRequestHandler

Vision Image Request parameter options
07:37
5 More Sections
About the Instructor
Yohann Taieb
4.3 Average rating
2,935 Reviews
67,045 Students
77 Courses
Apps Games Unity iOS Android Apple Watch TV Development

Yohann holds a Bachelor of Science Degree in Computer Science from FIU University. He has been a College instructor for over 10 years, teaching iPhone Development, iOS 11, Apple Watch development, Swift 3, Unity 3D, Pixel Art, Photoshop for programmers, and Android. Yohann also has plenty of ideas which naturally turned him into an entrepreneur, where he owns over 100 mobile apps and games in both the Apple app store and the Android store.

Yohann is one of the leading experts in mobile game programming, app flipping and reskinning. His teaching style is unique, hands on and very detailed. Yohann has enabled more than 50000 students to publish their own apps and reach the top spots in iTunes App Stores, which has been picked up by blogs and medias like WIRED magazine, Yahoo News, and Forbes Online. Thanks to him, thousands of students now make a living using iOS 11, Swift 4, Objective C ( ObjC ), Machine Learning, Augmented Reality / VIrtual Reality, Android, Apple Watch ( watchOS ), Apple TV ( TVOS ), Unity 3D, and Pixel art animation