Mô tả

Welcome to the Machine Learning use in React Native - The Practical Guide

Covering all the fundamental concepts of using ML models inside React Native applications, this is the most comprehensive React Native ML course available online.

The important thing is you don't need to know background working knowledge of Machine learning and computer vision to use ML models inside React Native and train them.

Starting from a very simple example course will teach you to use advanced ML models in your React Native ( Android & IOS ) Applications. So after completing this course you will be able to use both simple and advanced Tensorflow lite models in your React Native( Android & IOS ) applications.

Who can take this course

Anyone with a very little knowledge of app development in React Native with Expo or with React Native CLI. We will use React Native CLI but course will also guide you if you just have the expo knowledge.

Course structure

We will start by learning about an important library

  1. Image Picker: to choose images from the gallery or capture images using the camera in React Native

So later we can use a computer vision model with both images and live camera footage in React Native.


Then we will learn the use of popular pre-trained TensorFlow lite models inside React Native applications. So we explore some popular models and build the following React Native applications in this section

  • Image classification React Native application using images of gallery and camera

  • Image classification React Native application using live footage from the camera

  • Object detection React Native application using images of gallery and camera

  • Human pose estimation React Native application using images of gallery and camera

  • Image Segmentation React Native application using images of gallery and camera


After learning the use of pre-trained machine learning models inside React Native we will learn to train our own Image classification models without knowing any background knowledge of Machine Learning. So we will learn to

  • Gether and arrange the data set for the machine learning model training

  • Training Machine learning some platforms with just a few clicks

So in that section, we will

  • Train a dog breed classification model for React Native

  • Build a React Native( Android & IOS ) application to recognize different breeds of dogs

  • Train Fruit recognition model using Transfer learning

  • Building a React Native( Android & IOS ) application to recognize different fruits


So the course is mainly divided  into two major sections

  • Pretrained TensorFlow lite models for React Native

  • Training image classification models for React Native


So in the first section of this course, you will learn about using Tensorflow lite models inside React Native. Tensorflow Lite is a standard format for running ML models on mobile devices. So in this section, you will learn the use of pretrained powered ML models inside React Native for building

  • Image Classification React Native( ImageNet V2 model )

  • Object Detection  React Native( MobileNet model, Tiny YOLO model)

  • Pose Estimation  React Native( PostNet model )

  • Image Segmentation  React Native( Deeplab model )

applications. So not only you will learn to use these models with images but you will also learn to use them with frames of camera footage to build real-time React Native applications.

So after learning the use of Machine Learning models inside React Native using two different approaches in the third section of this course you will learn to train your own Machine Learning models without any background knowledge of machine learning. So in that section, we will explore some platforms that enable us to train machine learning models for mobile devices with just a few clicks. So in the third section, you will learn to

  • Collect and arrange the dataset for model training

  • Training the Machine Learning models from scratch using Teachable-Machine

  • Retraining existing models using Transfer Learning

  • Using those trained models inside React Native Applications

So we will train the models to recognize different breeds of dogs and to recognize different fruits and then build React Native Applications using those models for android and IOS.

By the end of this course, you will be able

  • Use pre-trained Tensorflow lite models inside Android & IOS applications using React Native

  • Train your own Image classification models and build React Native applications.

You'll also have a portfolio of over 10 React Native apps that you can show off to any potential employer.

Sign up today, and look forwards to:

  • HD 1080p video content, everything you'll ever need to succeed as a React Native Machine Learning developer.

  • Building over 10 fully-fledged React Native applications including ones that use Objet detection, Pose estimation models, and much much more.

  • All the knowledge you need to start building Machine Learning-based React Native(Android or IOS) application you want

  • $2000+ Source codes of 10 Applications.

REMEMBER… I'm so confident that you'll love this course that we're offering a FULL money-back guarantee for 30 days! So it's a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.

So what are you waiting for? Click the buy now button and join the world's best React Native Machine Learning course.

Who this course is for:

  • Beginner React Native developer with very little knowledge of mobile app development in React Native

  • Intermediate React Native developer wanted to build a powerful Machine Learning-based application in React Native

  • Experienced React Native developers wanted to use Machine Learning models inside their applications.

  • Anyone who took a basic React Native mobile app development course before

  • Anyone with knowledge of React Native App development with expo

  • Anyone with knowledge of React Native App development without expo (CLI)

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Yêu cầu

Nội dung khoá học

11 sections

Setting up the environment

4 lectures
Course Introduction
02:29
Setting up the code editor
01:47
Android studio and emulator
02:53
Creating an Android Emulator
01:09

Choosing and capturing images

5 lectures
Creating a new React Native Project
03:45
Creating GUI of the Application
05:43
Choosing images from gallery in React Native
08:54
Capturing images using camera in React Native
01:45
Overview
01:53

Pretrained Machine Learning Models Use in React Native

3 lectures
Section Introduction
02:01
Machine Leaning Introduction
04:25
Tensorflow lite Introduction
05:11

Image Classification

6 lectures
Section Introduction
02:37
Setting up the project
08:43
Adding model inside React native project
04:42
Performing image classification
07:55
Showing predictions on screen
10:43
What we have done so far
01:17

Image Classification live feed

6 lectures
Quantization
06:20
Creating and running a new react native project
03:17
Perofrming image classification with live camera footage
08:25
Showing live camera footage in react native
04:08
Showing predictions of model on screen
03:43
Overview and customisation
02:00

Object Detection

7 lectures
Object Detection Section Introduction
02:32
Setting up the starter application for object detection
07:37
Performing object detection in React Native
06:25
Understanding output format
05:36
Drawing rectangles around detected objects
10:35
Showing names of predictions
02:37
Object detection using YOLO
03:16

Pose Estimation

4 lectures
Pose Estimation Section Introduction
02:40
Importing starter application code
06:13
Performing pose estimation in react native using posenet model
03:53
Showing results to the user
10:04

Image Segmentation

4 lectures
Image Segmentation Section Introduction
03:37
Setting up the starter project for image segmentation
05:46
Performing Image Segmentation using Deeplab model
09:35
Clarifying some concepts
02:15

Training Machine Learning Models for React Native

2 lectures
Section Introduction
02:59
Training Image Classification Model
02:30

Training dog breed recognition model for React Native

6 lectures
How to find Image classification datasets
04:08
Arranging image classification dataset
03:33
Training an Image classification model
07:01
Testing and downloading the model
04:05
What we have done so far
01:29
Dog breed recognition with images
04:27

Transfer Learning

11 lectures
Fruit Recognition with Transfer Learning
02:57
Getting fruits dataset for model training
03:54
Uploading fruits dataset
02:48
Google colab
01:50
Training fruit recognition model
12:30
Saving trained model in drive
01:38
Directly uploading dataset on google colab
02:21
Exploring notebook further and testing tflite file
06:53
Retraining other models
04:45
Fruit recognition with Images
03:49
Fruit Recognition using live camera footage
03:59

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