Machine Learning for Apps
4.2 (248 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11,331 students enrolled

Machine Learning for Apps

Start building more intelligent apps with Machine Learning. Take advantage of this new foundational framework!
4.2 (248 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11,331 students enrolled
Last updated 10/2017
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 7 hours on-demand video
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn to code how the PROs code - not just copy and paste
  • Build Real Projects - You'll get to build projects that help you retain what you've learned
  • Build awesome apps that can make predictions
  • Build amazing apps that can classify human handwriting
Course content
Expand all 42 lectures 06:52:21
+ Intro to Course
4 lectures 30:47

In this lesson, you will learn the basics of Machine Learning in general – what it is and why developers care.

Preview 07:46

In this lesson, you will learn the 5 main steps in Machine Learning and how we will utilize them in this course.

Preview 06:34

In this lesson, you will install Anaconda – an application that makes creating and switching between Python environments seamless on your Mac.

Installing Anaconda / Python Environment
07:25

In this lesson, you will download and configure Atom – a fully hackable text editor we will use to write Python code in the following section.

Downloading / Setting Up Atom & Plugins
09:02
+ Python Basics
4 lectures 37:28

In this lesson, you will learn how to create and work with variables in Python.

Variables in Python
08:24

In this lesson, you will learn how to write and use functions, conditionals, and loops in Python.

Functions, Conditionals, & Loops in Python
09:50

In this lesson, you will learn how to create and use arrays and tuples in Python.

Arrays & Tuples in Python
13:52

In this lesson, you will learn how to import modules (think frameworks) in Python to grant access to additional functionality.

Importing Modules in Python
05:22
+ Building a Classification Model
8 lectures 01:07:44

In this lesson, you will become familiar with scikit-learn – a popular machine learning module in Python. You will learn what it is and why you should using it.

What is scikit-learn? Why use it?
03:52

In this lesson, you will install scikit-learn and scipy with Anaconda. Scipy is a framework for using Scientific Python.

Installing scikit-learn & scipy with Anaconda
03:28

In this lesson, you will be introduced to the Iris Dataset – a famous set of data used to classify three types of Iris flower.

Intro to the Iris Dataset
03:28

In this lesson, you will be given a definition and examples of Features & Labels – the two most important pieces of data required to train a machine learning model.

Datasets: Features & Labels Explained
07:39

In this lesson, you will load the Iris dataset into your Python project, examine the data, and make the necessary preparations for the data to be used for model training.

Loading the Iris Dataset / Examining & Preparing Data
09:27

In this lesson, you will learn about KNeighborsClassifier, create an instance of it, and train it with our array of training data.

Creating / Training a KNeighborsClassifier
09:42

In this lesson, you will test the accuracy of the Classification model using test data.

Testing Prediction Accuracy with Test Data
12:08

In this video, you will build your own KNeighborsClassifier class from scratch to understand how it works under the hood.

Building Our Own KNeighborsClassifier
18:00
+ Building a Convolutional Neural Network
8 lectures 02:02:06

In this lesson, you will be introduced to Keras – a robust, fully-featured Machine Learning framework you will use to create a neural network capable of classifying human handwriting.

What is Keras? Why use it?
08:01

In this lesson, you will learn about Convolutional Neural Networks (CNNs), how they work, and how we will use them.

What is a Convolutional Neural Network (CNN)?
26:30

In this lesson, you will install Keras using Anaconda then import it into your project.

Installing Keras with Anaconda
04:38

In this lesson, you will learn what is needed to prepare data to enter a CNN.

Preparing Dataset for a CNN
17:38

In part 1 of this lesson, you will build and visualize a CNN in code and by observing diagrams.

Building / Visualizing a CNN using Sequential: Part 1
14:07

In part 2 of this lesson, you will build and visualize a CNN in code and by observing diagrams.

Building / Visualizing a CNN using Sequential: Part 2
19:40

In this lesson, you will train your CNN, evaluate it's accuracy, and save the compiled model to your local disk.

Training CNN / Evaluating Accuracy / Saving to Disk
17:53

In this lesson, you will learn how to use Anaconda to switch Python environments and convert your Keras model into a Core ML model for use in Xcode.

Switching Python Environments / Converting to Core ML Model
13:39
+ Building a Handwriting Recognition App
6 lectures 01:13:39

In this video, you will be introduced to the handwriting analysis app you'll build using your hand-rolled Core ML model.

Intro to App – Handwriting
02:56

In this lesson, you will build the interface of your app in Interface Builder and wire up the required @IBOutlets/Actions.

Building Interface / Wiring Up
11:42

In this lesson, you will use the UITouch delegate methods to handle drawing on the screen.

Drawing On Screen
21:01

In this lesson, you will import your Core ML model and read through the metadata to ensure that everything was created as expected.

Importing Core ML Model / Reading Metadata
05:16

In this lesson, you will utilize Core ML and Vision to make a prediction based on input sent in from a drawing on the screen.

Utilizing Core ML / Vision to Make Prediction
17:31

In this lesson, you will process results returned from our Core ML request handler and write a function to convert the greatest value in our array of possible values into a presented digit on the screen.

Handling / Displaying Prediction Results
15:13
+ Core ML Basics
12 lectures 01:20:37

In this video, you will be introduced to the Core ML app you'll build in this Target Topic. It's an amazing photo analysis app that uses machine learning to identify images with a certain level of confidence.

Intro to App – Core ML Photo Analysis
04:25

In this lesson, you will learn the basics of Machine Learning in general – what it is and why developers care.

What is Machine Learning?
07:46

In this lesson, you will learn about Core ML – Apple's Machine Learning framework.

What is Core ML?
05:03

In this lesson, you will create the Xcode project needed to build the Core ML photo analysis app.

Creating Xcode Project
02:43

In this lesson, you will build out ImageVC in Interface Builder and connect the required @IBOutlets to certain UI elements.

Building ImageVC in Interface Builder / Wiring Up
07:40

In this lesson, you will build ImageCell – the UICollectionViewCell that will hold an image for our Core ML model to analyze later on in this course. You will create the code subclass as well and link up any needed @IBOutlets.

Creating ImageCell & Subclass / Wiring Up
08:13

In this lesson, you will create a helper file containing instance of UIImage with our imported image files. This static data will be used to populate the UICollectionView in ImageVC.

Creating FoodItems Helper File
07:02

In this lesson, you will create a custom UICollectionViewFlowLayout which will be used to set the UICollectionView to show a nice 3 column grid of square images.

Creating Custom 3x3 Grid UICollectionViewFlowLayout
09:12

In this lesson, you will visit developer.apple.com to select and download a pre-trained Core ML model for use in your project. You will learn how to import it successfully into Xcode and set it up for use as an ordinary Swift class.

Choosing, Downloading, Importing Core ML Model
05:18

In this lesson, you will learn how to pass images through a Core ML model using Core ML, Vision (an image-specific ML framework), and a series of requests, handlers, and results.

Passing Images Through Core ML Model
12:18

In this lesson, we will elegantly present the data returned by the Core ML model in the UILabel in the ImageVC interface when an ImageCell is selected.

Handling Core ML Prediction Results
09:42

In this video, you will be challenged to take what you've learned and add an extra feature to this Core ML powered app.

Challenge – Core ML Photo Analysis
01:15
Requirements
  • Must have a computer with OSX or macOS on it
Description

MACHINE LEARNING FOR APPS

Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech.

Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.

WHY TAKE THIS COURSE?

Core ML is the first step if you want to start building apps with AI. Machine Learning opens an entirely new world to opportunities that will take your apps to the next level.


Here are some of the things you'll be able to do after taking this course:

  • Learn to code how the PROs code - not just copy and paste
  • Build Real Projects - You'll get to build projects that help you retain what you've learned
  • Build awesome apps that can make predictions
  • Build amazing apps that can classify human handwriting

WHAT YOU WILL LEARN:

  • Learn about the foundation of Machine Learning and Core ML
  • Learn foundational python
  • Build a classification model allow your apps to make predictions
  • Build a neural network for your app that can classify human writing
  • Learn core ML concepts so you can build your own ML Model
  • Utilize the power of Machine Learning and AI for use in iOS apps
  • Learn how to pass in images to Apples pre trained model - MobileNet

Don't forget to join the free live community where you can get free help anytime from other students

Who this course is for:
  • If you have basic experience with iOS development take this course
  • If you have basic experience with iOS or mobile development then take this course