Flutter Artificial Intelligence Course Build 15+ AI Apps

Flutter Artificial Intelligence Course – Build 15+ AI Apps, available at $49.99, has an average rating of 4.15, with 108 lectures, based on 120 reviews, and has 1377 subscribers.

You will learn about Flutter Deep Learning Flutter Machine Learning Flutter Artificial Intelligence Skills and Techniques to develop any Artificial Intelligence idea into a mobile phone app Implementing (NLP) Natural Language Processing Algorithm for Mobile Apps Development Implementing (CNN) Convolutional Neural Network for Mobile Apps Development Optical Character Recognition Understanding of Different Types of Neural Networks & How you can use them you will learn and make 15+ Ai Apps and Much more. This course is ideal for individuals who are Anyone can join this course. It is particularly useful for Anyone can join this course.

Enroll now: Flutter Artificial Intelligence Course – Build 15+ AI Apps

Summary

Title: Flutter Artificial Intelligence Course – Build 15+ AI Apps

Price: $49.99

Average Rating: 4.15

Number of Lectures: 108

Number of Published Lectures: 108

Number of Curriculum Items: 108

Number of Published Curriculum Objects: 108

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Flutter Deep Learning

Flutter Machine Learning

Flutter Artificial Intelligence

Skills and Techniques to develop any Artificial Intelligence idea into a mobile phone app

Implementing (NLP) Natural Language Processing Algorithm for Mobile Apps Development

Implementing (CNN) Convolutional Neural Network for Mobile Apps Development

Optical Character Recognition

Understanding of Different Types of Neural Networks & How you can use them

you will learn and make 15+ Ai Apps

and Much more.

Who Should Attend

Anyone can join this course.

Target Audiences

Anyone can join this course.

In this course you will learn how to make your own Artificial Intelligence Apps using Flutter (Android+iOS) with TensorFlow Lite.

We will develop 15+ AI Apps with Flutter using TensorFlow Machine Learning and Deep Learning Concepts. In this course you will also learn how to train a model/machine for your apps. And how to import and use these trained models after training in your flutter app (android+iOS app).

This is a complete step by step course. At the end of this course you will be able to make your own Ai, Deep Learning and Machine Learning Apps for the Android Smart Phones and iOS [iPhones] using Flutter SDK with TensorFlow Lite.

TensorFlow Lite is an open source deep learning framework for on-device inference. See the guide. Guides explain the concepts and components of TensorFlow Lite.

TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Among these 15+ Apps we will also develop 3 additional apps using Firebase Machine Learning Kit which is also known as Firebase ML Kit.

Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Chapter 2: Complete Setup Download & Install Flutter SDK

Lecture 1: For Windows users – Flutter 2.2 Setup

Lecture 2: For MAC Users – Flutter 2.2 Setup

Chapter 3: Cat vs Dog Detector App

Lecture 1: Creating Project and Installing Dependences

Lecture 2: Adding SplashScreen

Lecture 3: Creating Home Page

Lecture 4: Home Page Design – Completed

Lecture 5: Downloading Dataset and Perform Training on Dataset – Get Trained Model

Lecture 6: Adding TFlite Functions

Lecture 7: Creating Functions for Capturing Image and Pick Image from Gallery

Lecture 8: Completing App and Texting the App

Lecture 9: Complete Project Source Code

Chapter 4: Face Mask Detection App

Lecture 1: create new project

Lecture 2: Add Splash Screen

Lecture 3: Implement Live Camera Feature

Lecture 4: Add and Load TfLite Model for Face Mask Detection

Lecture 5: Run Model on Camera Stream Frames

Lecture 6: Finishing the App and Testing the App

Lecture 7: Complete Project Source Code

Chapter 5: Cats Breed Identifier App

Lecture 1: Ceating and Setting up the project

Lecture 2: Completing the SplashScreen

Lecture 3: Completing the Home Page Design

Lecture 4: Creating Funtions for Uploading and Capturing Photos

Lecture 5: Download Model and Training it

Lecture 6: Installing Tflite and ImagePicker

Lecture 7: Adding Image Recognition Funtions and Testing our App

Lecture 8: Complete Project Source Code

Chapter 6: Flower Types Identifier App

Lecture 1: Setup the Project & Everything

Lecture 2: Installing TfLite in our App

Lecture 3: Downloading our Dataset and Training our Model

Lecture 4: Creating Funtions and Testing our app

Lecture 5: Complete Project Source Code

Chapter 7: Avengers Characters Recogniser App

Lecture 1: Setup the Project || Ui & Everything

Lecture 2: Download Dataset and Training our Model

Lecture 3: Installing TfLite in our application

Lecture 4: Creating Funtions and Testing our App

Lecture 5: Complete Project Source Code

Chapter 8: Image Captions Generator App – ((NLP) Natural Language Processing Algorithm)

Lecture 1: Create and Setting up Project

Lecture 2: Add Splash Screen

Lecture 3: HomeScreen part 1

Lecture 4: HomeScreen part 2

Lecture 5: HomeScreen part 3

Lecture 6: Implement Capture Image and Pick image from Gallery Functions

Lecture 7: Create API Service

Lecture 8: Get Image Caption Predictions Response from API

Lecture 9: Calling the getResponse Function

Lecture 10: Implement Live Camera Stream Function

Lecture 11: Finalising the App and Testing the App

Lecture 12: Project Complete Source Code

Chapter 9: Live Object Detection App

Lecture 1: Create and Setup New Project

Lecture 2: Install Required Packages

Lecture 3: Implement Live Camera Function

Lecture 4: Add Object Detection Model to our Project

Lecture 5: Load Model into our Flutter Project

Lecture 6: Run Model on Stream Frame

Lecture 7: Implement Boxes Around Detected Objects Function

Lecture 8: Finishing the App and Testing the App

Lecture 9: Complete Project Source Code

Chapter 10: Pose Estimation App

Lecture 1: Create and Setup Project

Lecture 2: Add Splash Screen

Lecture 3: Create Home Page

Lecture 4: Implement Live Camera Function

Lecture 5: Add Model and Load Model

Lecture 6: Run Model on Stream Frame

Lecture 7: Finish the App and Testing the App

Lecture 8: Complete Project Source Code

Chapter 11: Jarvis Objects Recogniser App – (Convolutional Neural Network (CNN) Algorithm)

Lecture 1: Create Project & Setup Everything

Lecture 2: Add Splash Screen

Lecture 3: Create Home Page

Lecture 4: Initialise Live Camera

Lecture 5: Implement Live Camera Function

Lecture 6: Add Model in our Project and Load Model

Lecture 7: Run Model on Camera Stream Frames

Lecture 8: Finish the App and Testing the App

Lecture 9: Complete Project Source Code

Chapter 12: Dogs Breed Identifier App

Lecture 1: Create and Setup New Project

Lecture 2: Splash Screen

Lecture 3: Implement Live Camera Feature for Dogs Breed Recognition

Lecture 4: Load Model and Run Model for Dogs Breed Live Identification

Lecture 5: Finishing the App and Testing the App

Lecture 6: Complete Project Source Code

Chapter 13: Fruits Recogniser App

Lecture 1: Create New Project and Installing Dependencies

Lecture 2: Splash Screen

Lecture 3: Implement Live Camera Feature

Lecture 4: Load Model and Run Model

Lecture 5: Finishing the App and Testing the App

Lecture 6: Complete Project Source Code

Instructors

Coding Cafe
Web and Mobile Applications Development

Rating Distribution

1 stars: 15 votes

2 stars: 8 votes

3 stars: 11 votes

4 stars: 29 votes

5 stars: 57 votes

Frequently Asked Questions

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