Android & Linear Regression – House Price Prediction App

Free Download Android & Linear Regression – House Price Prediction App
Published 11/2023
Created by Hamza Asif
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 61 Lectures ( 4h 48m ) | Size: 2.73 GB

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
Android studio installed in your PC
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!
Who this course is for
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

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