Machine Learning use in Android The Complete 2024 Guide

Machine Learning use in Android – The Complete 2024 Guide, available at $69.99, has an average rating of 4.15, with 307 lectures, 1 quizzes, based on 55 reviews, and has 433 subscribers.

You will learn about Learn use of Machine Learning & Computer Vision in Android App Development Train Machine Learning Models on Custom Datasets for Android Development Use Pre-Trained Tensorflow Lite Models in Android App Development Train Custom Image Classification Models and build Smart Android Apps Use of Tensorflow lite delegates in Android to improve model performance Use of Floating point and quantized models tensorflow lite models in Android Build Cam Scanner clone in Android Build A Text Recognition Application in Android Build A Face Detection and Facial Expression Detection Application in Android Build A Text Translation Application in Android Develop a Human Pose Estimation Application in Android Image Labeling / Image Classification in Android Perform Object Detection in Android with Images and Videos Add Smart Reply Suggestion Models in Chat based Android Apps Extract entities or valuable information from text in Android Create a barcode scanner app in Android Build Hand Writing Recognition Application in Android (Digital Ink Recognition) This course is ideal for individuals who are Beginner Android Developer curious about Machine learning and computer vision use in Android or Intermediate Android developers looking to enhance their skillset or Experienced Professional want to integrate Machine Learning in their Android Applications It is particularly useful for Beginner Android Developer curious about Machine learning and computer vision use in Android or Intermediate Android developers looking to enhance their skillset or Experienced Professional want to integrate Machine Learning in their Android Applications.

Enroll now: Machine Learning use in Android – The Complete 2024 Guide

Summary

Title: Machine Learning use in Android – The Complete 2024 Guide

Price: $69.99

Average Rating: 4.15

Number of Lectures: 307

Number of Quizzes: 1

Number of Published Lectures: 280

Number of Published Quizzes: 1

Number of Curriculum Items: 310

Number of Published Curriculum Objects: 283

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn use of Machine Learning & Computer Vision in Android App Development

Train Machine Learning Models on Custom Datasets for Android Development

Use Pre-Trained Tensorflow Lite Models in Android App Development

Train Custom Image Classification Models and build Smart Android Apps

Use of Tensorflow lite delegates in Android to improve model performance

Use of Floating point and quantized models tensorflow lite models in Android

Build Cam Scanner clone in Android

Build A Text Recognition Application in Android

Build A Face Detection and Facial Expression Detection Application in Android

Build A Text Translation Application in Android

Develop a Human Pose Estimation Application in Android

Image Labeling / Image Classification in Android

Perform Object Detection in Android with Images and Videos

Add Smart Reply Suggestion Models in Chat based Android Apps

Extract entities or valuable information from text in Android

Create a barcode scanner app in Android

Build Hand Writing Recognition Application in Android (Digital Ink Recognition)

Who Should Attend

Beginner Android Developer curious about Machine learning and computer vision use in Android

Intermediate Android developers looking to enhance their skillset

Experienced Professional want to integrate Machine Learning in their Android Applications

Target Audiences

Beginner Android Developer curious about Machine learning and computer vision use in Android

Intermediate Android developers looking to enhance their skillset

Experienced Professional want to integrate Machine Learning in their Android Applications

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 LabelingAndroid (Java & Kotlin)to recognize different things

Barcode ScanningAndroid (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 RecognitionAndroid (Java & Kotlin)to recognize handwritten text

Object DetectionAndroid (Java & Kotlin)to detect and track objects

Text RecognitionAndroid (Java & Kotlin)to recognize text in images

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

Text TranslationAndroid (Java & Kotlin)to translate between different languages

Face DetectionAndroid (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 Real-Time 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 Real-Time 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 Real-Time Android applications.

Android Version

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Java: Choose or capturing Images and Showing live camera footage

Lecture 1: Creating new Android Studio Project & GUI of of our Application

Lecture 2: Choosing Images From Gallery in Android

Lecture 3: One More Step

Lecture 4: Capturing Images from Camera in Android

Lecture 5: Converting Images into Bitmap in Android

Lecture 6: Image Picker in Android Overview

Lecture 7: Showing Live Camera footage in Android using Camera 2 API

Lecture 8: Testing Live Feed Application

Lecture 9: Android converting frames of live camera footage into Bitmap

Chapter 3: Kotlin : Choose or capturing Images and Showing live camera footage

Lecture 1: Creating a new Android Studio Project and building GUI of Application

Lecture 2: Choosing Images From Gallery In Android

Lecture 3: Handling Permissions in Android

Lecture 4: Capturing Images using Camera In Android

Lecture 5: Converting Images into Bitmaps in Android

Lecture 6: Image Picker in Android Overview

Lecture 7: Showing Live Camera footage in Android using Camera 2 API

Lecture 8: Testing Live Feed Application

Lecture 9: Android converting frames of live camera footage into Bitmap

Chapter 4: Firebase ML Kit

Lecture 1: Section introduction

Lecture 2: Firebase ML Kit Introduction

Chapter 5: Java: Image Labeling in Android

Lecture 1: Setting Up Image Labeling With Images Project

Lecture 2: GUI of Image Labeling With Images Android Application

Lecture 3: Documentation of Image Labeling

Lecture 4: Adding Library in Android and Preparing Images for Image Labeling

Lecture 5: Initializing Image Labeler and Performing Image Labeling

Lecture 6: Testing Image Labeling With Images Application

Lecture 7: Formatting Output and Setting Confidence threshold

Lecture 8: Image Labeling With Images Overview

Chapter 6: Kotlin: Image Labeling in Android

Lecture 1: Setting Up Image Labeling With Images Project

Lecture 2: GUI of Image Labeling With Images Android Application

Lecture 3: Documentation of Image Labeling

Lecture 4: Adding Library in Android and Preparing Images for Image Labeling

Lecture 5: Initializing Image Labeler and Performing Image Labeling

Lecture 6: Testing Image Labeling With Images Application

Lecture 7: Formatting Output and Setting Confidence threshold

Lecture 8: Image Labeling With Images Overview

Chapter 7: Pre-Trained Models Section

Lecture 1: Introduction

Lecture 2: Tensorflow lite Introduction

Chapter 8: Java Image Classification Section

Lecture 1: Image Classification Section Introduction

Lecture 2: Importing Starter application code for Image classification

Lecture 3: Explanation of starter application code

Lecture 4: Image classification application coding

Lecture 5: Testing Image Classification application

Lecture 6: Classifier class

Lecture 7: Importing starter code for Live Feed Image Classification

Lecture 8: Starter Application Demo

Lecture 9: Live camera Footage in Android

Lecture 10: Image classification with frames of live camera footage

Lecture 11: Testing live feed image classification application

Chapter 9: Kotlin Image Classification Section

Lecture 1: Image Classification Section Introduction

Lecture 2: Importing Image Labeling application starter code

Lecture 3: Image classification application coding

Lecture 4: Testing Image Classification application

Lecture 5: Classifier class

Lecture 6: Image Classification with Images Overview

Lecture 7: Importing Image Labeling live feed application starter code

Lecture 8: Starter Application Demo

Lecture 9: Image classification with frames of live camera footage

Lecture 10: Testing live feed image classification application

Chapter 10: Java: Quantization

Lecture 1: Quantization Introduction

Lecture 2: Using a quantized model in Android

Chapter 11: Kotlin: Quantization

Lecture 1: Quantization Introduction

Chapter 12: Java: Delegates

Lecture 1: Delegates Introduction

Lecture 2: Delegates in action

Lecture 3: Delegates documentation

Chapter 13: Kotlin: Delegates

Lecture 1: Delegates Introduction

Lecture 2: Delegates in action

Lecture 3: Delegates documentation

Chapter 14: Java Object Detection

Lecture 1: Object Detection Section introduction

Lecture 2: Importing Starter application for object detection

Lecture 3: Performing object detection in Android

Lecture 4: Drawing rectangles around detected objects

Lecture 5: Classifier class for Object Detection

Lecture 6: Importing live feed object detection application

Lecture 7: Object Detection Application Demo

Lecture 8: Object detection on frames of live camera footage

Chapter 15: Kotlin Object Detection

Lecture 1: Object Detection Section introduction

Lecture 2: Importing Starter application for object detection

Lecture 3: Performing object detection

Lecture 4: Drawing rectangles around detected objects

Lecture 5: Classifier class

Lecture 6: Quantized Model

Lecture 7: Importing live feed object detection application

Lecture 8: Live Feed Object Detection Application Demo

Instructors

Mobile ML Academy by Hamza Asif
ML & AI based Flutter, Android, IOS & React Native Courses

Rating Distribution

1 stars: 5 votes

2 stars: 2 votes

3 stars: 6 votes

4 stars: 14 votes

5 stars: 28 votes

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