Duration: 9h 16m | Video: .MP4 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | Size: 4.1 GB
Genre: eLearning | Language: English

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 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 application.

The class is divided into four main parts.

Images and Live Footage
Computer Vision model
Regression models
Training ML models
Images and Live Footage

In the first part, we will learn to deal with images and live camera footage in Android, so that later we can use them with machine learning models. So we will learn to

Choose Images from the gallery
Capture images using the camera
Showing live camera footage in Android using camera2 API
Computer vision models

In the second section, you will learn the use of popular pre-trained computer vision models in Android and build

Image classification
Object detection
Image segmentation

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
Use the use of Tensorflow lite Delegates to improve model performance
Regression In Android

After that, we will learn to use regression models in Android and build a couple of applications including a

Fuel Efficiency Predictor for Vehicles.
Training Image Classification Models

After mastering the use of ML Models in Android in the Third section we will learn to train our own Image Classification models without knowing any background knowledge of Machine learning.

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 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 application using that trained model
Android Version

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


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


These are tools we will be using throughout the course

Android Studio to develop Android Applications
Google colab to train Image Recognition models.
Netron to analyze mobile machine learning models
By the end of this course, you will be able

Use of images and live camera footage in Android
Use pre-trained Tensorflow lite vision models inside Android applications using Java and Kotlin
Use of Regression models in Android
Train your own Image classification models and build Android applications.
You'll also have a portfolio of over 10+ machine learning-based Android applications that you can show to any potential employer.

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 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? Let's begin.


Download link:
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