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 Dog's 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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