Machine Learning for Apps | Course Description | Alison

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Machine Learning for Apps

Learn the basics of how to use Python for machine learning and iOS app development with this free online course.

Mobile Apps
Free Course
This free online machine learning course covers foundational python for machine learning. You'll learn what machine learning (ML) is and how it is applied, study the five steps of ML integration, and look into the installation of Anaconda and Atom including their applications. You'll learn how to build an iOS app and create a classification model capable of making predictions using neural networks, and much more!
  • Duration

    5-6 Hours
  • Assessment

  • Certification

  • Responsive

  • Publisher






View course modules


This free course covers foundational Python, the language most predominantly used in machine learning (ML). The course will show you how to build a classification model from scratch to make predictions, using a world-famous data set to train the model. You'll create a neural network, capable of classifying human handwriting, and then use your neural network to build an iOS app, that can classify handwritten digits drawn on the screen.

The course also shows you how to use an ML model to classify photos in a custom-built app in module six. By the end of this course, you'll be able to apply the concepts and tools learned, to create your own machine learning models and apps for making suggestions and predictions.

Machine learning skills are in high demand worldwide as they can be used in many different ways: doctors can use it to diagnose diseases; it can be used to match people for relationships; it can be used to analyse costly manufacturing procedures. The number one reason for using machine learning in an app, may be that it personalises the app for the user. So start the course today and by the end of the week you'll have gained valuable skills in machine learning and real-world programming skills for using machine learning to build apps.

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Essential Software

Python Basics

Classification Modelling

Convolutional Neural Network

Building the App

Core ML Basics

Course assessment

Learning Outcomes

Having completed this course you will be able to:

  • Explain what machine learning (ML) is and how it is applied
  • Declare and work with basic Python variables
  • Program basic arrays and tuples in Python
  • Define the two most important characteristics of data for machine learning
  • Describe how data is used to train and test the learning model
  • Discuss the benefits of using Keras rather than TensorFlow
  • Describe how convolutional neural networks classify images
  • Explain the role of max pooling layers, sequential and ReLU
  • Outline the function of dropout layers, nodes and softmax
  • Define how epochs measure accuracy and loss
  • Create an Xcode project and configure an input field, text label and button for an iOS app
  • Provide examples of how core machine learning can be used
  • Create a new Xcode project, including folders and assets


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