Master Android Image Recognition: Build AI-Powered Kotlin Apps

What you will learn:

Build custom image classification models from scratch.
Convert trained models to the TensorFlow Lite format.
Integrate custom models into Android apps using Kotlin.
Use image pickers and the Camera2 API in Android.
Perform real-time image classification on Android devices.
Employ transfer learning for improved model accuracy.
Handle datasets efficiently for model training.
Develop user-friendly interfaces for AI-powered Android apps.
Master the fundamentals of machine learning and deep learning.
Build three distinct image classification projects.

Description

Transform your Android development skills with our in-depth course on building intelligent image recognition apps using Kotlin. This course isn't just about using pre-built models; we'll empower you to create custom image classification models from the ground up, tailored to your unique needs. We cover the entire process, from foundational machine learning concepts to seamless integration within Android applications.

What You'll Master:

Machine Learning & Deep Learning Fundamentals: Lay a solid groundwork in machine learning and deep learning principles specifically tailored for image recognition.
Dataset Creation & Management: Learn practical strategies for gathering, organizing, and preparing your data for optimal model training. We'll explore techniques for finding existing datasets and handling your own data.
Model Training Techniques: We'll explore two powerful methods: the user-friendly Teachable Machine platform for quick prototyping, and advanced transfer learning to achieve higher accuracy and efficiency. Learn to leverage pre-trained models to boost your results.
TensorFlow Lite Integration: Master the process of converting your trained models into the TensorFlow Lite format, optimized for speed and efficiency on Android devices.
Android App Development with Kotlin: Build robust Android applications that seamlessly integrate your custom models. We'll cover both image selection from a gallery and the sophisticated Camera2 API for real-time image processing.
Real-Time Image Recognition: Develop apps that analyze images and video from the camera feed, providing instant classification results.

Projects to Boost Your Portfolio:

Produce a Fruit & Vegetable Classifier: Build an app to identify various fruits and vegetables.
Develop a Medical Image Analysis Tool: Create a model to classify brain tumor images (using a publicly available, ethically sourced dataset).
Build a Flower Identifier: Design an app that accurately identifies different flower species.

By the course's conclusion, you will be equipped to construct advanced, AI-powered applications that utilize image and video recognition capabilities. Prepare to create cutting-edge mobile solutions – enroll today!

Curriculum

Introduction

This introductory section lays the groundwork for the course, starting with a general introduction to the course content and then delving into the world of image classification, discussing its applications and the importance of machine learning in this field. The lectures cover core concepts and provide a solid base for subsequent sections.

Machine Learning & Deep Learning for Image Classification

This section dives into the fundamental concepts of machine learning and deep learning. Topics covered include supervised and unsupervised machine learning, regression and classification techniques, an introduction to neural networks, and how neural networks are used for image classification. The section builds a comprehensive understanding of the theoretical underpinnings of the course.

Data Collection

Efficient data collection is crucial for successful model training. This section covers finding ready-to-use datasets and the processes involved in preparing those datasets for effective model training, along with techniques for dealing with image datasets and preparing them for processing. The final lecture addresses the specific requirements and strategies for collecting and preparing a dataset for brain tumor classification using MRI images.

Training Your First Custom Image Classification Model

This section provides a hands-on introduction to model training using Teachable Machine, a user-friendly platform ideal for quickly building custom models. The process of uploading datasets, training models, testing their accuracy, and converting them into the TensorFlow Lite format is explained. The section also introduces Google Colab, a useful tool for model training, and demonstrates the importance of attaching metadata to trained models.

Training Custom Image Classification Model with Transfer Learning

This section introduces transfer learning, an advanced technique leveraging pre-trained models to improve accuracy and efficiency. It covers setting up Google Colab, installing libraries, uploading datasets, and dividing the dataset into training, testing, and validation sets. The section culminates in training a custom image classification model using transfer learning and converting it to TensorFlow Lite format.

Training Brain Tumor Classification Model

This section focuses on building a model specific to brain tumor classification, utilizing the techniques and knowledge gained from previous sections. The emphasis is on applying learned skills to a real-world medical application.

Android App Development

This section initiates the Android app development phase, providing a general introduction before moving into more specific aspects of Android app building and integrating models

Image Picker Android

This section covers choosing images from the Android gallery and capturing images using the device's camera. It teaches secure data handling using Android's File Provider.

Image Classification With Images

This section focuses on integrating the TensorFlow Lite model into the Android app. It covers loading the model, passing input image data, and displaying the classification results on the user interface.

Background Of Using Tensorflow Lite Models in Android

This section explores the underlying mechanics of using TensorFlow Lite models within an Android application, delving into the model loading process, input/output handling, and converting raw model output into user-friendly results.

Using Transfer Learning Trained Model in Android & GUI Improvements

This section focuses on using transfer learning trained model in Android and improves the graphical user interface of the image classification app.

Display Live Camera Footage in Android with Camera2 API

This section covers displaying real-time camera footage using Android's Camera2 API, focusing on obtaining camera permission, displaying the live feed, extracting image frames as bitmaps, and efficient image processing within the application.

Realtime Image Classification Android

This final section brings together all previously learned concepts to create a real-time image classification app using the Camera2 API, integrating trained TensorFlow Lite models for live image analysis and refining the app's user interface. It also covers setting a confidence threshold for classification accuracy.