Android & Linear Regression: House Price Prediction App

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

What you'll learn

Train linear regression models for Android Applications

Integrate regression models in Android Applications

Use of Tensorflow Lite models in Android

Train Any Prediction Model & use it in Android 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 Android Applications

Requirements

Android studio installed in your PC

Description

Welcome to the exciting world of Android 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 Android app development. Whether you're a seasoned Android developer or new to the scene, this course has something valuable to offer youCourse 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 Android.The Android-ML Fusion: After grasping the core concepts, we'll bridge the gap between Android 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 trainingUnlocking 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 AndroidCourse Highlights:Training Your First Regression Model:Harness TensorFlow and Python to create a simple regression modelConvert the model into TFLite format, making it compatible with AndroidLearn to integrate the regression model into Android apps Fuel Efficiency Prediction:Apply your knowledge to a real-world problem by predicting automobile fuel efficiencySeamlessly integrate the model into an Android app for an intuitive fuel efficiency prediction experienceHouse Price Prediction in Android:Master the art of training regression models on substantial datasetsUtilize the trained model within your Android app to predict house prices confidentlyThe Android Advantage: By the end of this course, you'll be equipped to:Train advanced regression models for accurate predictionsSeamlessly integrate regression models into your Android applicationsAnalyze and use existing regression models effectively within the Android ecosystemWho Should Enroll:Aspiring Android developers eager to add predictive modeling to their skillsetEnthusiasts seeking to bridge the gap between Machine Learning and mobile app developmentData aficionados interested in harnessing the potential of data for real-world applicationsStep into the World of Android and Predictive Modeling: Join us on this exciting journey and unlock the potential of Android and Linear Regression. By the end of the course, you'll be ready to develop Android applications that not only look great but also make informed, data-driven decisions.Enroll now and embrace the fusion of Android and predictive modeling!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Machine Learning & Deep Learning Introduction

Lecture 2 What is Machine Learning

Lecture 3 Supervised Machine Learning: Regression & Classification

Lecture 4 Unsupervised Machine Learning & Reinforcement Learning

Lecture 5 Deep Learning and regression models training

Lecture 6 Basic Deep Learning Concepts

Section 3: Python: A simple overview

Lecture 7 Google Colab

Lecture 8 Python Introduction & its datatypes

Lecture 9 Lists in Python

Lecture 10 Dictionary and Tuples in Python

Lecture 11 Loops and Conditional Statements in Python

Lecture 12 File Handling In Python

Section 4: Data Science Libraries : Numpy, Pandas, Matplotlib

Lecture 13 Numpy Library

Lecture 14 Operations in Numpy

Lecture 15 Functions in Numpy

Lecture 16 Pandas library

Lecture 17 Loading CSV Files in Pandas

Lecture 18 Handling missing values in Pandas dataset

Lecture 19 Matplotlib library

Lecture 20 Images in Matplotlib

Section 5: Tensorflow and Tensorflow Lite

Lecture 21 Tensorflow : Variables & Constants

Lecture 22 Tensorflow: Shapes & Ranks of Tensors

Lecture 23 Ragged Tesnors & Matrix Multiplication in Tensorflow

Lecture 24 Tensorflow Operations

Lecture 25 Random Values in Tensorflow

Lecture 26 Tensorflow Checkpoints: Save ML models

Section 6: Train a simple Regression Model and build Android Application

Lecture 27 Training a simple regression model for mobile devices

Lecture 28 Model Testing and Conversion into Tensorflow Lite

Lecture 29 Tensorflow Lite Model Training Overview

Lecture 30 Analysing trained tflite model

Lecture 31 Creating a new Android Studio Project and GUI of Application

Lecture 32 Adding Tensorflow Lite Library In Android & Loading Tensorflow Lite Model

Lecture 33 Passing Input to Tensorflow Lite Model in Android and Getting Output

Lecture 34 Using basic tflite regression model in Android overview

Section 7: Fuel Efficiency Prediction: Training an advance regression model

Lecture 35 Section Introduction

Lecture 36 Data Collection: Finding Fuel Efficiency Prediction Dataset

Lecture 37 Loading Dataset in Python for Model Training

Lecture 38 Handling missing Values in Fuel Efficiency Prediction Dataset

Lecture 39 Handling Categorical Columns in Dataset for Model Training

Lecture 40 Dataset Normalization

Lecture 41 Training Fuel Efficiency Prediction Model in Tensorflow

Lecture 42 Testing Trained Model and converting it to Tensorflow Lite Model

Lecture 43 Training Fuel Efficiency Prediction Model Overview

Section 8: Fuel Efficiency Prediction Android Application

Lecture 44 Setting up Android Application for fuel efficiency prediction

Lecture 45 Starter Application Overview

Lecture 46 Loading Tensorflow Lite models in Android

Lecture 47 Data Normalization in Android

Lecture 48 Passing input to Tensorflow Lite model in Android and getting output

Lecture 49 Testing fuel efficiency prediction android application

Lecture 50 Fuel Efficiency Prediction Android App Overview

Section 9: Training a house price prediction Model

Lecture 51 Section Introduction

Lecture 52 Getting dataset for training house price prediction model

Lecture 53 Loading dataset for training tflite model

Lecture 54 Training & Evaluating house price prediction model

Lecture 55 Retraining House Price Prediction Model

Section 10: Building House Price Prediction Android Application

Lecture 56 Setting Up Android Studio Project

Lecture 57 What we have done so far

Lecture 58 Data Normalization in Android

Lecture 59 Passing Input to house price prediction model in Android

Lecture 60 Testing house price prediction Android Application

Beginner Android Developer who want to build Machine Learning based Android Applications,Aspiring Android 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