Mastering Machine Learning in React Native: Build 10+ Smart Mobile Apps

What you will learn:

Integrate machine learning models into React Native apps
Utilize TensorFlow Lite models for mobile applications
Train custom image classification models for React Native
Build real-time image classification and object detection apps
Implement image segmentation and pose estimation in React Native
Develop intelligent Android and iOS applications with ML
Master both Expo and React Native CLI for ML app development
Create robust image handling techniques for mobile ML
Deploy and optimize TensorFlow Lite models for mobile performance
Build a portfolio of 10+ ML-powered React Native applications

Description

Transform your React Native skills and build the next generation of intelligent mobile apps! This comprehensive course dives deep into integrating machine learning (ML) models into your iOS and Android projects. No prior ML experience is needed; we'll guide you step-by-step through practical examples and projects.

Learn to leverage pre-trained TensorFlow Lite models for image classification, object detection, pose estimation, and image segmentation. You'll then master the art of training your own custom ML models without complex coding, using user-friendly platforms. Build real-time applications processing camera feeds and create 10+ fully functional React Native apps.

This course is perfect for beginner and intermediate React Native developers, offering a clear path to creating innovative, ML-powered applications. Master the use of both Expo and React Native CLI, building confidence to tackle any mobile ML challenge.

What you'll achieve:

Gain a solid understanding of TensorFlow Lite and its integration with React Native.
Build 10+ complete React Native applications showcasing different ML models.
Master the process of training your own ML models using readily available tools.
Develop real-time applications utilizing camera input for ML processing.
Create a strong portfolio showcasing your new expertise to potential employers.

This course includes:

HD 1080p video tutorials.
Over 10 fully functional React Native applications including source code.
Extensive support and guidance to ensure your success.
A 30-day money-back guarantee, so you can learn with confidence.

Enroll now and unlock your potential as a mobile ML developer!

Curriculum

Setting up the Development Environment

This section lays the groundwork for your mobile ML journey. Lectures cover course introduction, setting up your preferred code editor (like VS Code), configuring Android Studio and setting up an Android emulator, ensuring a seamless development process. You'll have everything ready to start building your app by the end.

Image Acquisition and Handling

Learn essential skills for integrating image data into your apps. You'll start a new React Native project, design its user interface, learn how to select images from your device's gallery, capture images directly with the camera, and understand an overview of these fundamental processes. This section builds a robust foundation for later integration of ML models.

Introduction to Machine Learning and TensorFlow Lite

This introductory section provides a clear understanding of fundamental Machine Learning concepts. Lectures dive into what makes TensorFlow Lite a powerful tool for mobile deployment, ensuring you can effectively utilize this core technology throughout the course.

Image Classification with Pre-trained Models

Dive into image classification using pre-trained TensorFlow Lite models. Lectures cover project setup, integrating the model into your React Native application, performing the classification process, visualizing predictions on the screen, and summarizing what you’ve achieved so far. You'll build a functional image classifier.

Real-time Image Classification with Live Camera Feed

Learn the essentials of image processing and applying models to live camera feeds. This section explains quantization for efficient model processing, creates and runs a React Native project which integrates live camera input for real-time processing, displays the live footage, shows the model's predictions, and provides an overview with customization options.

Object Detection with Pre-trained Models

This section focuses on object detection. Lectures explore setting up the starting application, performing object detection within React Native, understanding output formats, visually displaying detected objects with bounding boxes, labeling the detected objects, and using the efficient YOLO model.

Human Pose Estimation

Learn to build applications that analyze human poses. Lectures cover importing the starter code, using the PoseNet model for pose estimation, and effectively presenting the results to the user. This section adds another powerful ML capability to your toolkit.

Image Segmentation

This section delves into image segmentation using the DeepLab model. Lectures cover project setup, performing image segmentation, and clarifying any related concepts. This advanced technique opens up new possibilities for your applications.

Training Your Own Machine Learning Models

This pivotal section empowers you to create your own ML models without extensive background knowledge. Lectures cover the fundamentals of training an image classification model.

Dog Breed and Fruit Recognition Model Training

Build custom models for dog breed and fruit recognition. Lectures cover finding and organizing datasets, training image classification models, testing, downloading, and utilizing them within React Native projects. You'll build applications for both image recognition and live camera feed processing, showcasing your learned capabilities.

Transfer Learning for Enhanced Model Training

This section focuses on transfer learning to accelerate model training. Lectures cover obtaining fruit datasets, uploading them, using Google Colab, training the fruit recognition model, saving the trained model, using direct dataset uploading to Google Colab, exploring further notebook functionalities, testing, and retraining. You'll also learn to build React Native applications using these trained models.