Welcome to Machine Learning use in Android the Complete Guide.

In this course, you will learn the use of Machine learning and computer vision in Android along with training your own image recognition models for Android applications without knowing any background knowledge of machine learning. The course is designed in such a manner that you don't need any prior knowledge of machine learning to take this course.

In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used. So it’s important to learn how we can integrate ML models inside Android (Java & Kotlin) applications. And this course will teach you that. And the main feature of this is you don’t need to know any background knowledge of ML to integrate it inside your Android applications.

What we will cover in this course?

1. Dealing with Images in Android

2. Dealing with frames of live camera footage in Android

3. Use of quantized and floating point tensorflow lite models in Android

4. Use of tensor flow lite delegates to improve the performance of ML models in Android

5. Image classification with images and live camera footage in Android

6. Object Detection with Images and Live Camera footage

7. Image Segmentation to make images transparent in Android

8. Use of regression models in Android

9. Image Labeling Android to recognize different things

10. Barcode Scanning Android to scan barcodes and QR codes

11. Pose Estimation Android to detect human body joints

12. Selfie Segmentation Android to separate the background from the foreground

13. Digital Ink Recognition Android to recognize handwritten text

14. Object Detection Android to detect and track objects

15. Text Recognition Android to recognize text in images

16. Smart Reply Android to add auto reply suggestion

17. Text Translation Android to translate between different languages

18. Face Detection Android to detect faces, facial landmarks, and facial expressions

19. Training image classification models for Android

20. Retraining existing machine learning and computer vision models with transfer learning for Android applications

Sections:

The course is divided into four main parts.

• Image and live camera footage in Android (Java & Kotlin)

• Pre-Trained Tensorflow Lite models use in Android (Java & Kotlin)

• Firebase ML Kit use in Android (Java & Kotlin)

• Training Image Classification models for Android (Java & Kotlin)

1: Images and live camera footage in Android (Java & Kotlin)

So in the first section, you will learn to handle both images and live camera footage in Android so that later we can use them with machine learning models. So, in that section, we will learn to

• Choose images from the gallery in Android (Java & Kotlin)

• Capture images using the camera in Android (Java & Kotlin)

• Displaying live camera footage in Android (Java & Kotlin) applications using camera2 API

• Accessing frames of live camera footage in Android (Java & Kotlin)

2: Pre-Trained Tensorflow Lite

So, after learning the use of images and live camera footage in Android in this section we will learn the use of popular pre-trained machine learning and computer vision models in Android and build

• Image classification Android app (Both with images and live camera footage)

• Object detection Android app(Both with images and live camera footage)

• Image segmentation Android

applications

3: Quantization and Delegates

Apart from that, we will cover all the important concepts related to Tensorflow lite like

• Using floating-point and quantized model in Android (Java & Kotlin)

• Use the use of Tensorflow lite Delegates to improve model performance

4: Regression In Android

After that, we will learn to use regression models in Android (Java & Kotlin) and build a couple of applications including a

• Fuel Efficiency Predictor for Vehicles.

  
  

5: Firebase ML Kit

Then the next section is related to the Firebase ML Kit. In this section, we will explore

• Firebase ML Kit

• Features of Firebase ML Kit

Then we are going to explore those features and build a number of applications including

• Image Labeling Android (Java & Kotlin) to recognize different things

• Barcode Scanning Android (Java & Kotlin) to scan barcodes and QR codes

• Pose Estimation Android (Java & Kotlin) to detect human body joints

• Selfie Segmentation Android (Java & Kotlin) to separate the background from the foreground

• Digital Ink Recognition Android (Java & Kotlin) to recognize handwritten text

• Object Detection Android (Java & Kotlin) to detect and track objects

• Text Recognition Android (Java & Kotlin) to recognize text in images

• Smart Reply Android (Java & Kotlin)to add auto reply suggestion

• Text Translation Android (Java & Kotlin) to translate between different languages

• Face Detection Android (Java & Kotlin) to detect faces, facial landmarks, and facial expressions

CamScanner Android Clone

Apart from all these applications, we will be developing a clone of the famous document scanning android application CamScanner. So in that application, we will auto crop the document images using text recognition and improve the visibility of document Images.

6: Training Image Classification Models

After mastering the use of ML Models in the Android (Java & Kotlin) app development in the Third section we will learn to train our own Image Classification models without knowing any background knowledge of Machine learning and computer vision.

So in that section, we will learn to train ML models using two different approaches.

Dog breed Recognition using Teachable Machine

• Firstly we will train a dog breed recognition model using a teachable machine.

• Build a Live Feed Dog Breed Recognition Android (Java & Kotlin) Application.

Fruit Recognition using Transfer Learning

• Using transfer learning we will retrain the MobileNet model to recognize different fruits.

• Build a live feed fruit recognition Android (Java & Kotlin) application using that trained model

Images and Live Camera Footage

The course will teach you to use Machine learning and computer vision models with images and live camera footage, So that, you can build both simple and live feed Android applications.

Android Version

The course is completely up to date and we have used the latest Android 11 (R) throughout the course.

Language

The course is developed using both Java and Kotlin programming languages. So all the material is available in both languages.

Tools:

These are tools we will be using throughout the course

• Android Studio for Android App development

• Google collab to train Image Recognition models.

• Netron to analyze mobile machine learning models

By the end of this course, you will be able

• Use Firebase ML kit in Android App development using both Java and Kotlin

• Use pre-trained Tensorflow lite models in Android App development using Java and Kotlin

• Train your own Image classification models and build Android applications.

You'll also have a portfolio of over 20+ machine learning and computer vision-based Android R applications that you can show to any potential employer.

course requirements:

This is the course for you if

• You want to make smart Android (Java & Kotlin) apps

• You are interested in becoming a modern-day Android (Java & Kotlin) developer, a freelancer, launching your own projects, or just want to try your hand at making real smart mobile apps

• You have no prior programming experience, or some but from a different language/platform

• You want a course that teaches you the use of machine learning and computer vision in Android (Java & Kotlin) app development, in an integrated curriculum that will give you a deep understanding of all the key concepts an Android (Java & Kotlin) developer needs to know to have a successful career

  
  

Who can take this course:

• Beginner Android ( Java or Kotlin ) developer with very little knowledge of Android app development.

• Intermediate Android ( Java or Kotlin ) developer wanted to build a powerful Machine Learning-based application in Android

• Experienced Android ( Java or Kotlin ) developers wanted to use Machine Learning and computer vision models inside their Android applications.

• Anyone who took a basic Android ( Java or Kotlin ) mobile app development course before (like Android ( Java or Kotlin ) app development course by angela yu or other such courses).

Unlike any other Android app development course, The course will teach you what matters the most.

So what are you waiting for? Click on the Join button and start learning.