Machine Learning for Apps

0
Certificate

Paid

Language

Level

Intermediate

Access

Free

Last updated on March 2, 2024 12:38 am

In this course, learn foundational Python for machine learning and how to build an iOS app and classification model capable of making predictions.

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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!

What You Will Learn In This Free Course

  • Explain what machine learning (ML) i…
  • Declare and work with basic Python v…
  • Program basic arrays and tuples in P…
  • Define the two most important charac…
  • 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
  • Essential Software

    In this module you’ll learn about essential software for machine learning and it covers topics such as What is Machine Learning?; Machine Learning Basics; Installing Anaconda; Atom and Plugins.

    Python Basics

    In this module you’ll learn about the basics of Python, and it covers topics such as Variables in Python; Python Functions, Conditionals and Loops; Python Arrays and Tuples; Modules in Python.; Module

    Classification Modelling

    In this module you’ll learn about classification modelling and it covers topics such as Installing SciKit-Learn; Iris Flower Dataset; Dataset Features and Labels; Preparing Data; Training a Classifier; Testing Prediction Accuracy; Building a Classifier.

    Convolutional Neural Network

    In this module you’ll learn about convolutional neural networks and it covers topics such as Installing Keras and PIP; Preparing the Dataset; Using Sequential; Sequential Process; Training the Data; Core ML Model.

    Building the App

    In this module you’ll learn about building the app and it covers topics such as Building the Interface; Drawing On Screen; Core ML Import; Utilizing Core ML; Displaying Prediction Results.; Module

    Core ML Basics

    In this module you’ll learn about the basics of Core ML and it covers topics such as Core ML Photo Analysis; New Xcode Project; Building the ImageVC; ImageCell and Subclass; Food Items Helper File; Custom Grid; Importing Core ML Model; Passing In Images; Handling Prediction Results. NOTE: The final video topic is a learning challenge to develop a new feature for the app.

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      Machine Learning for Apps
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