Flutter And Linear Regression: Build Prediction Apps Flutter

Train regression models for Flutter | Use regression models in Flutter | Tensorflow Lite models integration in Flutter

What you'll learn

Train regression models for Mobile Applications

Integrate regression models in Flutter for both Android & IOS

Use of Tensorflow Lite models in Flutter

Train Any Prediction Model & use it in Flutter Applications

Data Collection & Preprocessing for model training

Basics of Machine Learning & Deep Learning

Understand the working of artificial neural networks for model training

Basic syntax of python programming language

Use of data science libraries like numpy, pandas and matplotlib

Analysing & using advance regression models in Flutter Applications

Requirements

Android studio & Flutter installed in your PC

Description

Welcome to the exciting world of Flutter and Linear Regression! 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 delve into the world of deep learning and artificial neural networks, which will serve as the foundation for training our regression models in 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 regression 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.Course Highlights:Training Your First Regression Model:Harness TensorFlow and Python to create a simple regression model.Convert the model into TFLite format, making it compatible with Flutter.Learn to integrate the regression model into Flutter apps for Android and iOS.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.House Price Prediction in Flutter:Master the art of training regression models on substantial datasets.Utilize the trained model within your Flutter app to predict house prices confidently.The Flutter Advantage: By the end of this course, you'll be equipped to:Train advanced regression models for accurate predictions.Seamlessly integrate regression models into your Flutter applications.Analyze and use existing regression models effectively within the Flutter ecosystem.Who Should Enroll:Aspiring Flutter developers eager to add predictive modeling to their skillset.Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.Data aficionados interested in harnessing the potential of data for real-world applications.Step into the World of Flutter and Predictive Modeling: Join us on this exciting journey and unlock the potential of Flutter and Linear Regression. 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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Curriculum

Section 2: Machine Learning & Deep Learning

Lecture 3 Machine Learning Introduction

Lecture 4 Supervised Machine Learning: Regression & Classification

Lecture 5 Unsupervised Machine Learning & Reinforcement Learning

Lecture 6 Deep Learning and regression models training

Lecture 7 Basic Deep Learning Concepts

Section 3: Python Programming Language

Lecture 8 Google Colab Introduction

Lecture 9 Python Introduction & data types

Lecture 10 Python Lists

Lecture 11 Python dictionary & tuples

Lecture 12 Python loops & conditional statements

Lecture 13 File handling in Python

Section 4: Data Science Libraries

Lecture 14 Numpy Introduction

Lecture 15 Numpy Operations

Lecture 16 Numpy Functions

Lecture 17 Pandas Introduction

Lecture 18 Loading CSV in pandas

Lecture 19 Handling Missing values in dataset with pandas

Lecture 20 Matplotlib & charts in python

Lecture 21 Dealing images with Matplotlib

Section 5: Tensorflow

Lecture 22 Tensorflow Introduction | Variables & Constants

Lecture 23 Shapes & Ranks of Tensors

Lecture 24 Matrix Multiplication & Ragged Tensors

Lecture 25 Tensorflow Operations

Lecture 26 Generating Random Values in Tensorflow

Lecture 27 Tensorflow Checkpoints

Section 6: Training a basic regression model

Lecture 28 Section Introduction

Lecture 29 Train a simple regression model for Flutter

Lecture 30 Testing model and converting it to a tflite(Tensorflow lite) format

Lecture 31 Model training for flutter overview

Lecture 32 Creating a new flutter project

Lecture 33 Adding libraries and loading regression models in Flutter

Lecture 34 Passing Input to regression model and getting output in Flutter

Lecture 35 Regression Models Integration in Flutter Overview

Section 7: Training a Fuel Efficiency Prediction Model

Lecture 36 Section Introduction

Lecture 37 Getting datasets for training regression models

Lecture 38 Loading dataset in python with pandas

Lecture 39 Handling Missing Values in Dataset

Lecture 40 One Hot Encoding: Handling categorical columns

Lecture 41 Training and testing datasets

Lecture 42 Normalization: Bringing all columns to a common scale

Lecture 43 Training a fuel efficiency prediction model

Lecture 44 Testing fuel efficiency prediction model and converting it to a tflite format

Lecture 45 Fuel Efficiency Model Training Overview

Section 8: Fuel Efficiency Prediction Flutter Application

Lecture 46 Analyse trained fuel efficiency prediction model

Lecture 47 Set Up Starter Application for Fuel Efficiency Prediction

Lecture 48 What we have done so far

Lecture 49 Loading Tensorflow Lite model in Flutter for fuel efficiency prediction

Lecture 50 Normalizing user inputs in Flutter before passing it to our model

Lecture 51 Passing Input to our model and getting output

Lecture 52 Testing Fuel Efficiency Prediction Flutter Application

Lecture 53 Fuel Efficiency Prediction Flutter Overview

Section 9: Training House Price Prediction Model

Lecture 54 Section Introduction

Lecture 55 Getting house price prediction dataset

Lecture 56 Load dataset for training house price prediction regression model

Lecture 57 Training & evaluating house price prediction model

Lecture 58 Retraining price prediction model

Section 10: House Price Prediction Flutter Application

Lecture 59 Analysing house price prediction tensorflow lite model

Lecture 60 Loading house price prediction model in Flutter

Lecture 61 Passing input to tensorflow lite model and getting output

Lecture 62 Testing house price prediction Flutter Application

Beginner Flutter Developer who want to build Machine Learning based Flutter Applications,Aspiring Flutter developers eager to add predictive 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