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Train Image Classification Models, build Android Apps(2025)

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  • 4,020 Students
  • Updated 9/2025
4.3
(14 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
4 Hour(s) 52 Minute(s)
Language
English
Taught by
Mobile ML Academy by Hamza Asif
Rating
4.3
(14 Ratings)
2 views

Course Overview

Train Image Classification Models, build Android Apps(2025)

Train Custom Image Classification Models from Scratch | Use Image Recognition Models in Android 16 with Images or Videos

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 16 and Image Recognition .

Course Content

  • 10 section(s)
  • 52 lecture(s)
  • Section 1 Introduction
  • Section 2 Machine Learning & Deep Learning for Flutter
  • Section 3 Data Collection - Collecting Dataset for Training Image Classification Model
  • Section 4 Train Your First Custom Image Classification Model in 15 Minutes
  • Section 5 Training Custom Image Classification Model with Transfer Learning
  • Section 6 Training Brain Tumor Classification Model
  • Section 7 Android App Development
  • Section 8 Image Picker Android - Choose or Capture Images
  • Section 9 Image Classification With Images
  • Section 10 Background Of Using Tensorflow Lite Models in Android

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


Reviews

  • L
    Luca Musolesi
    4.0

    Esattamente quello che cercavo come progetto, peccato che lo volevo in java e non in kotlin

  • G
    Gulam Mohd
    5.0

    Easy to understand.

  • M
    Mario Daniel Salas Mendoza
    4.5

    I learned a lot from the course, I recommend it.

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