The complete Firebase ML Kit for Android App Development

Requirements

• You should have some basic knowledge of Android App Development using Java

Firebase ML Kit for Android Developer’s

Make your Android Applications smart, use ML trained model or train your own ML models explore the power of AI and Machine Learning.

This course was recorded using Android Studio 3.6.1 (which is a great introduction to the development environment!) For a smooth experience I’d recommend you use the same, but students can still use the latest Android Studio version available if they prefer!

Wish you’d thought of Object Recognition/Face Detection/Text Recognition?

Me too.

But until I work out how to build a time machine.

Here’s the next best thing.

Firebase ML Kit for Android Developer’s

Curriculum:

In this course, we will explore the features of Firebase ML Kit for Android. We will start by learning about Firebase ML Kit and Features it provides. Then we will see how to integrate ML Kit inside your Android Application just using Android studio. After that, we will explore the features of ML Kit and develop Android Applications like

• Text Recognition Android Application
• Android Application to Translate between Languages
• Language Detection Application
• Face Detection Application
• Barcode Scanner Android Application
• Object Detection Android App
• Landmark Recognition Application
• Stones Recognition Application

Then we will learn about Auto ML Vision edge feature of Firebase ML Kit using which we can train Machine Learning model on our own dataset and build Android Application for that model. We will train model to recognize different types of stones and build an Android App for that model.

At the end of this course, we will combine different features of Firebase ML kit to build an Android Application to categorize images of mobile gallery.

Why choose me?

My name’s Hamza Asif, Udemy’s coding instructor.

It’s not my first course on mobile Machine Leaning. I have a course named “Complete Tensorflow Lite course for Android App Development” on udemy.

So which course you should take?

It’s recommended taking “Machine Learning for Android Developer using Tensorflow lite” first so that you can understand the working of Machine Learning.

If you want to learn a practical implementation and use of Machine Learning in Android using Firebase ML Kit……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………. then that course is for you.

This is my 2nd course on Android Machine Learning and I am the only udemy instructor with more than one course on that topic. My goal is to promote the use of Machine Learning in Android and I am excited to share my knowledge with you.

Android Version we will use?

Android Pie, Android Q

All the Android Application we will develop in this course we will use Android Pie and Q to test them. So we are

So join my Firebase ML Kit for Android Developer’s course today and here’s what you’ll get

• Learn practical implementation of Text Recognition, Language Identification, Face and expression detection, Barcode scanning, Landmark Recognition, Text Translation, and Object detection and recognition inside Android App Development using Android Studio and ML kit.
• Learn how to use Auto ML to train the model on your own dataset and use those models in Android Application
• Learn about both on-device and Cloud Machine Learning

Why take this course?

Machine Learning use is at its peak so is the mobile tech but people having skills to implement both are rare. This course will enable you to empower your Android Applications with the practical implementation of Machine Learning, Computer Vision, and AI.

Having a little knowledge of Android App Development, this course will differentiate you from other developers because you will have something that is currently in demand.

This course will make provide you a smooth path to become a pro in using Machine Learning in your Applications.

This course will not just enable you to apply machine learning in limited scenarios but It will enable you to

• Prepare or download your own dataset
• Train machine learning model
• Develop Android Application

So if you have very basic knowledge of Android App Development and want to apply Machine Learning in Android Applications without knowing background knowledge of  Machine Learning this course is or you.

Is this course for you?

This is a one-size-fits-all course for beginners to experts. So, this course is for you if you are:

• A total beginner, with a curious mind and a drive to make and create awesome stuff using Android App development and ML
• A fledgling developer, want to add Machine Learning implementation in his skillset
• A pro app developer-heavyweight, with an itch to build your dream app
• An entrepreneur with big ideas

Benefits to you

• Risk-free! 30-day money-back guarantee
• Freedom to work from anywhere (beach, coffee shop, airport – anywhere with Wi-Fi)
• Potential to work with forward-thinking companies (from cool start-ups to pioneering tech firms)
• Rocket-fuelled job opportunities and powered-up career prospects
• A sense of accomplishment as you build amazing things
• Make any Android app you like (your imagination is your only limit)
• Submit your apps to Google Play and potentially start selling within hours
• Use ML Kit just using Android Studio

Thanks for getting this far. I appreciate your time! I also hope you’re as excited to get started as I am to share the latest use of ML in Android development with you.

All that remains to be said, is this…

Don’t wait another moment. The world is moving fast. And I know you’ve got ideas worth sharing.

Coding really can help you achieve your dreams.

So click the button to sign up today – completely risk-free.

And join me on this trailblazing adventure, today.

Who this course is for:

• Anyone who wants to learn the practical implementation of Machine Learning and Computer Vision in their Android Applications.
• Anyone who wants to make their Android App Development smart.
• Anyone who wants to train and deploy Machine Learning models on his own data without background knowledge of Machine Learning.