Train Image Classification Models, build Android Kotlin Apps

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Hamza Asif

4:52:48

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1. Introduction

1. Introduction.mp4

02:57

2. Image Classification Introduction & Applications.mp4

05:19

2. Machine Learning & Deep Learning for Flutter

1. What is Machine Learning.mp4

03:15

2. Supervised Machine Learning.mp4

03:33

3. Regression and Classification.mp4

02:08

4. Unsupervised Machine Learning & Reinforcement Learning.mp4

03:19

5. Deep Learning and Neural Network Introduction.mp4

05:56

6. Neural Network Example.mp4

10:05

7. Working of Neural Networks for Image Classification.mp4

04:51

8. Basic Concepts of Machine Learning.mp4

04:49

3. Data Collection - Collecting Dataset for Training Image Classification Model

1. Data Collection Introduction.mp4

03:25

2. Finding ready to use dataset for training image classification models.mp4

05:13

3.1 dataset.zip

3. Exploring Downloaded dataset for training custom image classification models.mp4

03:05

4. Find and Download A Dataset for Brain Tumor Classification using MRI.html

4. Train Your First Custom Image Classification Model in 15 Minutes

1. Section Introduction.mp4

01:22

2. Exploring Teachable Machine and Uploading Dataset for Model Training.mp4

04:57

3. Training, Testing and Converting Model into Tensorflow Lite.mp4

07:25

4. Attaching Metadata with Trained Tensorflow Lite Models.mp4

01:47

5. Google Colab Introduction.mp4

06:43

6.1 metadata writer tutorial.zip

6. Attaching Metadata and Downloading Ready to Use Model.mp4

05:09

5. Training Custom Image Classification Model with Transfer Learning

1. Transfer Learning Introduction.mp4

03:32

2. Google Colab Introduction.mp4

06:43

3.1 2024 training image classification models.zip

3. Installing and Importing Libraries for Model Training.mp4

04:27

4. Uploading Dataset and Connecting Google Drive.mp4

03:56

5. Dividing dataset into train test and validation parts.mp4

06:08

6. Training Custom Image Classification Model.mp4

09:14

7. Testing the model and Converting it to Tensorflow Lite Format.mp4

04:05

6. Training Brain Tumor Classification Model

1. Train Brain Tumor Classification Model.html

7. Android App Development

1. Section Introduction.mp4

01:29

8. Image Picker Android - Choose or Capture Images

1. Creating a new Android Studio Project and Building GUI of Android App.mp4

09:44

2. Choosing Images from Gallery in Android.mp4

07:22

3. Capturing Images using Camera in Android.mp4

09:52

4. File Provider Share Data Between Android Apps Securely.mp4

07:17

5. Capturing Images in Android Overview.mp4

02:35

9. Image Classification With Images

1.1 4 Image Classification With Images.zip

1.2 imagepicker.zip

1. Adding Tesnorflow Lite Model & Libraries in Android.mp4

06:18

2. Analyzing and loading Tesnorflow Lite Model in Android.mp4

05:00

3. Passing Input to tflite model and getting output in Android.mp4

06:17

4. Showing Results of Custom Image Classification Model on Screen in Android.mp4

05:18

10. Background Of Using Tensorflow Lite Models in Android

1. Loading Tensorflow Lite Model in Android.mp4

04:27

2. Passing input to the Tesnorflow Lite model and Getting output.mp4

04:14

3. How Tensorflow Lite Models return Results in Android.mp4

04:02

4. Converts Model Output into Results in Android.mp4

09:18

11. Using Transfer Learning Trained Model in Android & GUI Improvements

1. Using Transfer Learning Trained Model in Android.mp4

02:41

2. Improving GUI of Image Classification with Images Application.mp4

14:39

12. Display Live Camera Footage in Android with Camera2 API

1. Creating New Android Project and handling Camera Permission.mp4

10:41

2.1 Realtime Camera Footage.zip

2. Displaying Live Camera Footage in Android with Camera2 API.mp4

13:59

3. How we are displaying Camera in Android.mp4

05:17

4. Getting Frames of Live Camera Footage as Bitmaps in Android.mp4

10:41

13. Realtime Image Classification Android

1.1 RealTimeImageClassification.zip

1. Adding Models and Libraries in Android Studio Proect.mp4

05:08

2. Loading Tensorflow Lite Models in Android and Passing Frames of Camera.mp4

06:33

3. Showing Models results on Screen in Android.mp4

05:44

4. Using Transfer Learning Trained Model in Android.mp4

02:35

5. Setting Confidence Threshold in Android.mp4

00:52

6. Working on GUI of Realtime Image Classification Android Application.mp4

07:22

Description

Train Custom Image Classification Models from Scratch | Use Image Recognition Models in Android with Images or Videos

What You'll Learn?

Train Custom Image Classification Models from Scratch & Convert models into Android compatible tensorflow lite format
Use Custom Image Classification Models in Android with Images and Camera Footage
Collect Datasets for Training Custom Image Classification Models
Use Transfer Learning to Retrain Existing Image Classification Models and use them in Android
Train Custom Image Classification Models for Android using Two Different Approaches

Who is this for?

Beginner Android Developers looking to build Machine Learning Powered Android Apps

Anyone who want to train Image Classification Models and than use them in Android Apps

Android Developers looking to enhance their skills by learning to train and use image classification models in Android

What You Need to Know?

No ML and Data Science Knowledge Required

A very little knowledge of Android App Development

More details

Description
Unlock the full potential of mobile app development with our comprehensive course on training custom image Recognition models and integrating them into Android applications. This course is designed to guide you from the basics of machine learning and deep learning to creating sophisticated, real-time image recognition apps in Android Kotlin.
What You Will Learn:
Introduction to Machine Learning and Deep Learning: Start with the foundational concepts of machine learning, deep learning, and image Recognition to build a strong base for your journey.
Dataset Collection: Learn effective methods to collect and prepare datasets for training your image Recognition models.
Model Training Approaches: Train image Recognition models using two powerful approaches:
Teachable Machine: A user-friendly platform to create custom models.
Transfer Learning: Advanced technique to leverage pre-trained models for better accuracy and efficiency.
Tensorflow Lite Conversion: Convert your trained models into TensorFlow Lite format, making them compatible with mobile applications.
Android Integration: Seamlessly integrate your models into Android apps:
Image Recognition : Choose or capture images in Android and use your models for accurate image recognition.
Real-Time Camera Footage: Display live camera footage in Android, pass frames to your models, and build real-time, intelligent mobile apps.

Projects Included:
Fruit and Vegetable Classification Model: Create an app that identifies different fruits and vegetables.
Brain Tumor Classification Model: Develop a model to classify brain tumor images.
Flower Classification Model: Build a system to recognize various types of flowers.

By the end of this course, you'll be able to:
Train custom image Recognition models tailored to your specific needs.
Seamlessly integrate your models into Android applications built with Kotlin.
Craft intelligent mobile apps that leverage real-time image recognition functionalities.

So join us to become proficient in Android app development and create cutting-edge mobile apps with image and video recognition capabilities using Kotlin.
Enroll now and start your journey towards mastering Android and Image Recognition .
Who this course is for:
Beginner Android Developers looking to build Machine Learning Powered Android Apps
Anyone who want to train Image Classification Models and than use them in Android Apps
Android Developers looking to enhance their skills by learning to train and use image classification models in Android

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Android Development

Hamza Asif

Instructor's Courses

Experienced Mobile Developer, specialized in Mobile Machine Learning using Tensorflow lite, ML Kit, and Google cloud vision API. Leading Android Machine learning instructor with over 50,000 students from 150 countries. I am an enthusiastic developer with a strong programming background and possess great app development skills. I have developed a bunch of native and cross-platform apps in the past and satisfied all of my clients. It has been +4 years doing Mobile development and providing support for Android Applications. Empowering mobile Applications using Machine Learning and Computer vision is my core skill.Powering Android Application with ML really fascinates me. So I learned Android development and then Machine Learning. I developed Android applications for several multinational organizations. Now I want to spread the knowledge I have. I'm always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses.

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