Giter Site home page Giter Site logo

machine-learning-for-android-app-development-using-ml-kit's Introduction

Machine Learning for Android App development Using ML Kit [Video]

This is the code repository for Machine Learning for Android App development Using ML Kit [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

It's crazy to see how Artificial Intelligence and Machine Learning are moving so fast and becoming the next big thing. The focus is on putting AI and Machine Learning into people's hands in their daily lives by bringing it to their mobile devices.

ML Kit makes it easy to apply ML techniques to your apps by bringing Google's ML technologies together in a single SDK. With ML Kit you can have features such as text recognition, face recognition, barcode scanning, image labeling, and landmark recognition at your fingertips in your apps. In this course, you are going to implement all these features in your Android applications using ML Kit.

After completing this course, you will be confident enough to build Android applications equipped with in-built Machine Learning features, providing an amazing user experience. You will be able to go into the world and create your own useful Machine Learning apps using ML Kit. All the codes are present at: https://github.com/PacktPublishing/Machine-Learning-for-Android-App-development-Using-ML-Kit

What You Will Learn

  • Explore how machine learning is changing the world we live in.
  • Configure UIs with camera settings and use them in your app.
  • Implement text recognition and deploy it with Firebase on the cloud.
  • Perform face detection by adding it to your app and trying it out!
  • Scan through barcodes by adding the barcode scanning feature to your app.
  • Identify images by image labeling and deploy them with Firebase on the cloud.
  • Add features such as landmark recognition to your apps to identify a specific landmark.

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This course is for Android developers who want to try their hand at building a machine learning app using the new ML Kit SDK that Google recently released. This course does not require any previous knowledge of machine learning, as a basic introduction will be given so that you can fully understand the content.

Technical Requirements

This course has the following software requirements:

  • Android Studio

This course has been tested on the following system configuration:

  • OS:OS X High Sierra
  • Processor:Intel Core i7
  • Memory:16GB

Related Products

machine-learning-for-android-app-development-using-ml-kit's People

Contributors

packt-itservice avatar sanjeetkumar13 avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.