Android & Linear Regression: Train ML models for Android | Udemy


Android & Linear Regression: Train ML models for Android | Udemy
English | Size: 2.73 GB
Genre: eLearning

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

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 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 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 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 Android

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 Android

Learn to integrate the regression model into Android apps

Fuel Efficiency Prediction:

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

Seamlessly integrate the model into an Android app for an intuitive fuel efficiency prediction experience

House Price Prediction in Android:

Master the art of training regression models on substantial datasets

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

The Android 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 Android applications

Analyze and use existing regression models effectively within the Android ecosystem

Who Should Enroll:

Aspiring Android 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 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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