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Flutter & ML : Train Tensorflow Lite models for Flutter Apps

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Flutter & ML : Train Tensorflow Lite models for Flutter Apps, available at $54.99, has an average rating of 4.67, with 140 lectures, based on 24 reviews, and has 294 subscribers.

You will learn about Train Machine Learning models for Flutter Applications Train Image Classification and Object Detection Models for Flutter Apps Train Linear Regression Models for Flutter Apps Integrate Tensorflow Lite models in Flutter for both Android & IOS Use Computer Vision Models in Flutter with both Images and Live Camera Footage Train a machine learning model and build a fuel efficiency prediction Flutter Application Train a machine learning model and build a house price prediction Flutter Application Analysing & using advance regression models in Flutter Applications Train Any Prediction Model & use it in Flutter Applications Data Collection & Preprocessing for ML model training for Flutter Application Basics of Machine Learning & Deep Learning for training Machine learning Models for Flutter Understand the working of artificial neural networks for training machine learning for Flutter Basic syntax of Python programming language to train ML models for Flutter Use of data science libraries like numpy, pandas and matplotlib Train a fruit classification model and build a Fruit Recognition Flutter Application This course is ideal for individuals who are Beginner Flutter Developer who want to train ML models and build Machine Learning based Flutter Applications or Aspiring Flutter developers eager to add ML modeling to their skillset or Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development. or Machine Learning Engineers looking to build real world applications with Machine Learning Models It is particularly useful for Beginner Flutter Developer who want to train ML models and build Machine Learning based Flutter Applications or Aspiring Flutter developers eager to add ML modeling to their skillset or Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development. or Machine Learning Engineers looking to build real world applications with Machine Learning Models.

Enroll now: Flutter & ML : Train Tensorflow Lite models for Flutter Apps

Summary

Title: Flutter & ML : Train Tensorflow Lite models for Flutter Apps

Price: $54.99

Average Rating: 4.67

Number of Lectures: 140

Number of Published Lectures: 135

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 135

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • Train Machine Learning models for Flutter Applications
  • Train Image Classification and Object Detection Models for Flutter Apps
  • Train Linear Regression Models for Flutter Apps
  • Integrate Tensorflow Lite models in Flutter for both Android & IOS
  • Use Computer Vision Models in Flutter with both Images and Live Camera Footage
  • Train a machine learning model and build a fuel efficiency prediction Flutter Application
  • Train a machine learning model and build a house price prediction Flutter Application
  • Analysing & using advance regression models in Flutter Applications
  • Train Any Prediction Model & use it in Flutter Applications
  • Data Collection & Preprocessing for ML model training for Flutter Application
  • Basics of Machine Learning & Deep Learning for training Machine learning Models for Flutter
  • Understand the working of artificial neural networks for training machine learning for Flutter
  • Basic syntax of Python programming language to train ML models for Flutter
  • Use of data science libraries like numpy, pandas and matplotlib
  • Train a fruit classification model and build a Fruit Recognition Flutter Application

Who Should Attend

  • Beginner Flutter Developer who want to train ML models and build Machine Learning based Flutter Applications
  • Aspiring Flutter developers eager to add ML modeling to their skillset
  • Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.
  • Machine Learning Engineers looking to build real world applications with Machine Learning Models

Target Audiences

  • Beginner Flutter Developer who want to train ML models and build Machine Learning based Flutter Applications
  • Aspiring Flutter developers eager to add ML modeling to their skillset
  • Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.
  • Machine Learning Engineers looking to build real world applications with Machine Learning Models

Do you want to train different Machine Learning models and build smart Android & IOS applications in Flutter then Welcome to this course.

In this course, you will learn to

  • train powerful image classification, object detection, and linear regression models in Python from scratch

  • Then we will use these models in Flutter to build smart Flutter Apps

Regression is one of the fundamental techniques in Machine Learning which can be used for countless applications. Like you can train Machine Learning models using regression

  • to predict the price of the house

  • to predict the Fuel Efficiency of vehicles

  • to recommend drug doses for medical conditions

  • to recommend fertilizer in agriculture

  • to suggest exercises for improvement in player performance

and so on. So Inside this course, you will learn to train your custom machine learning models for Flutter and build smart Android & IOS applications in Flutter.

I’m Muhammad Hamza Asif, and in this course, we’ll embark on a journey to combine the power of predictive modeling with the flexibility of Flutter app development. Whether you’re a seasoned Flutter developer or new to the scene, this course has something valuable to offer you

Course Overview: We’ll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Flutter.

The Flutter-ML Fusion: After grasping the core concepts, we’ll bridge the gap between Flutter and Machine Learning. To do this, we’ll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model training

Unlocking Data’s Power: To prepare and analyze our datasets effectively, we’ll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data’s potential for accurate predictions.

Tensorflow for Mobile: Next, we’ll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including Flutter

Regression Models Training

  1. Training Your First Machine Learning Model:

    • Harness TensorFlow and Python to create a simple linear regression model

    • Convert the model into TFLite format, making it compatible with Flutter

    • Learn to integrate the tflite model into Flutter apps for Android and iOS

  2. Fuel Efficiency Prediction:

    • Apply your knowledge to a real-world problem by predicting automobile fuel efficiency

    • Seamlessly integrate the model into a Flutter app for an intuitive fuel efficiency prediction experience

  3. House Price Prediction in Flutter:

    • Master the art of training machine learning models on substantial datasets

    • Utilize the trained model within your Flutter app to predict house prices confidently

Computer Vision Model Training

  1. Image Classification in Flutter:

    • Collect and process dataset for model training

    • Train image classification models on custom datasets

    • Use image classification models in Flutter with both images and live camera footage

  2. Object Detection in Flutter

    • Use object detection models of ML Kit in Flutter

    • Train Custom Image Classification models and use them to classify detected objects

    • Perform Object Detection with both Images and Live Camera Footage

The Flutter Advantage: By the end of this course, you’ll be equipped to:

  • Train advanced machine learning models for accurate predictions

  • Seamlessly integrate tflite models into your Flutter applications

  • Analyze and use existing regression & vision (ML) models effectively within the Flutter ecosystem

Who Should Enroll:

  • Aspiring Flutter developers eager to add predictive modeling to their skillset

  • Beginner Flutter ( Dart ) developer with very little knowledge of mobile app development in Google Flutter

  • Intermediate Flutter ( Dart ) developer wanted to build a powerful Machine Learning-based application in Google Flutter

  • Experienced Flutter ( Dart ) developers wanted to use Machine Learning models inside their applications.

Step into the World of Flutter and Predictive Modeling: Join us on this exciting journey and unlock the potential of Flutter and Machine Learning. By the end of the course, you’ll be ready to develop Flutter applications that not only look great but also make informed, data-driven decisions.

Enroll now and embrace the fusion of Flutter and predictive modeling!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Machine Learning & Deep Learning for Flutter

Lecture 1: What is Machine Learning

Lecture 2: Supervised Machine Learning

Lecture 3: Regression and Classification

Lecture 4: Unsupervised Machine Learning & Reinforcement Learning

Lecture 5: Deep Learning and Neural Network Introduction

Lecture 6: Neural Network Example

Lecture 7: Basic Deep Learning Concepts

Chapter 3: Python Programming Language for Flutter

Lecture 1: Google Colab Introduction

Lecture 2: Python Introduction & data types

Lecture 3: Python Numbers

Lecture 4: Python Strings

Lecture 5: Python Lists

Lecture 6: Python dictionary & tuples

Lecture 7: Python loops & conditional statements

Lecture 8: File handling in Python

Chapter 4: Data Science Libraries for Flutter

Lecture 1: Numpy Introduction

Lecture 2: Numpy Functions and Generating Random Values

Lecture 3: Numpy Operators

Lecture 4: Matrix Multiplications and Sorting in Numpy

Lecture 5: Pandas Introduction

Lecture 6: Loading CSV in pandas

Lecture 7: Handling Missing values in dataset with pandas

Lecture 8: Matplotlib & charts in python

Lecture 9: Dealing images with Matplotlib

Chapter 5: Tensorflow & Tensorflow Lite for Flutter

Lecture 1: Tensorflow Introduction | Variables & Constants

Lecture 2: Shapes & Ranks of Tensors

Lecture 3: Matrix Multiplication & Ragged Tensors

Lecture 4: Tensorflow Operations

Lecture 5: Generating Random Values in Tensorflow

Lecture 6: Tensorflow Checkpoints

Lecture 7: Tensorflow Lite Introduction & Advantages

Chapter 6: Training a basic regression model for Flutter

Lecture 1: Section Introduction

Lecture 2: Train a simple regression model for Flutter

Lecture 3: Testing model and converting it to a tflite(Tensorflow lite) format for Flutter

Lecture 4: Model training for flutter app development overview

Chapter 7: Setup for MacOS

Lecture 1: Install the Flutter SDK

Lecture 2: Install Android Studio

Lecture 3: Install and Setup XCode

Lecture 4: Creating A Flutter Project and Installing in IOS Simulator

Lecture 5: Install the Android Emulator

Chapter 8: Setup for Windows

Lecture 1: Installing Flutter on Windows

Lecture 2: Installing Android Studio

Lecture 3: Creating Android Virtual Device

Chapter 9: Using First Regression Model in Flutter

Lecture 1: Creating a new flutter project

Lecture 2: Adding libraries and loading regression models in Flutter

Lecture 3: Passing Input to regression model and getting output in Flutter

Lecture 4: Regression Models Integration in Flutter Overview

Chapter 10: Training a Fuel Efficiency Prediction Model for Flutter

Lecture 1: Section Introduction

Lecture 2: Getting datasets for training regression models for Flutter

Lecture 3: Loading dataset in python with pandas

Lecture 4: Handling Missing Values in Dataset

Lecture 5: One Hot Encoding: Handling categorical columns

Lecture 6: Training and testing datasets

Lecture 7: Normalization Introduction

Lecture 8: Normalization: Bringing all columns to a common scale

Lecture 9: Training a fuel efficiency prediction model for Flutter

Lecture 10: Testing fuel efficiency prediction model and converting it to a tflite format

Lecture 11: Fuel Efficiency Model Training Overview

Chapter 11: Fuel Efficiency Prediction Flutter Application

Lecture 1: Analyse trained fuel efficiency prediction model for Flutter

Lecture 2: Setup Starter Flutter Application for Fuel Efficiency Prediction

Lecture 3: What we have done so far

Lecture 4: Loading Tensorflow Lite model in Flutter for fuel efficiency prediction

Lecture 5: Normalizing user inputs in Flutter before passing it to our model

Lecture 6: Passing Input to our model and getting output in Flutter Application

Lecture 7: Testing Fuel Efficiency Prediction Flutter Application

Lecture 8: Fuel Efficiency Prediction Flutter Overview

Chapter 12: Training House Price Prediction Model for Flutter

Lecture 1: Section Introduction

Lecture 2: Getting house price prediction dataset

Lecture 3: Load dataset for training house price prediction regression model for Flutter

Lecture 4: Training & evaluating house price prediction model for Flutter

Lecture 5: Retraining price prediction model

Chapter 13: House Price Prediction Flutter Application

Lecture 1: Analysing house price prediction tensorflow lite model

Lecture 2: Loading house price prediction model in Flutter

Lecture 3: Passing input to tensorflow lite model and getting output

Chapter 14: Training Our First Image Classification Model for Flutter

Lecture 1: Image Classification Introduction

Lecture 2: Object Detection Introduction

Lecture 3: Section Introduction

Lecture 4: Dataset Collection Introduction

Lecture 5: Downloading Dataset for Training our first Image Classification Model

Lecture 6: Training Our First Custom Image Classification Model for Flutter

Lecture 7: Testing Our Own Trained Model and Converting it to tflite format

Lecture 8: Hyperparameter tuning: Improving Model Accuracy

Lecture 9: Google Colab Introduction

Lecture 10: Attaching Metadata With a Trained Tensorflow Lite Model

Chapter 15: Training Models for Flutter Using Transfer Learning in Google Colab

Instructors

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  • 5 stars: 17 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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