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Train ML Regression Models & Build Predictive Android Apps

Name: Train ML Regression Models & Build Predictive Android Apps
Rating: 5.0 (3 reviews)

Build Data-Driven Android Apps Using Machine Learning, Tensorflow, TensorFlow Lite, and Predictive Modeling in 2025

Created byMobile ML Academy by Hamza Asif

Last updated 9/2025

English

What you'll learn

Train Machine Learning models for Android Applications
Analysing & using advance regression models in Android Applications
Train a machine learning model and build a house price prediction Android Application
Train a machine learning model and build a fuel price prediction Android Application
Integrate Machine Learning 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 for Android App Development
Basics of Machine Learning & Deep Learning for Android App Development
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

Course content

10 sections • 67 lectures • 4h 57m total length

Introduction3:11

What is Machine Learning3:15
Explore how machine learning uses data and algorithms to imitate human learning and make predictions. See dog breed recognition and salary prediction to illustrate data collection and model training.
Supervised Machine Learning3:33
Regression and Classification2:08
Unsupervised Machine Learning & Reinforcement Learning3:19
Learn unsupervised learning with unlabeled data and clustering to discover patterns, and explore reinforcement learning as a trial-and-error method where AI agents learn from rewards and penalties.
Deep Learning and Neural Network Introduction5:56
Explore deep learning and artificial neural networks, including neurons and layers, to extract patterns, train models, and understand classification and regression with gender and house price examples.
Neural Network Example10:05
Explore how a neural network predicts house price from area and rooms, using feed forwarding, backpropagation, and epoch-based training to adjust weights.
Basic Deep Learning Concepts4:49

Google Colab6:43
Python Introduction & its datatypes4:05
Python Numbers2:36
Python Strings2:49
Lists in Python6:50
Explore Python lists, the versatile data type that can hold mixed values and nested lists. Learn indexing, slicing, and common methods like append, insert, remove, count, sort, and reverse.
Dictionary and Tuples in Python3:11
Loops and Conditional Statements in Python4:40
File Handling In Python4:44

Tensorflow : Variables & Constants6:29
Tensorflow: Shapes & Ranks of Tensors6:50
Explore shaping and reshaping TensorFlow Lite tensors, inspect shapes like 2x3, rank and index elements, convert to numpy, and perform elementwise operations such as division and multiplication.
Ragged Tesnors & Matrix Multiplication in Tensorflow4:51
Explore tensor operations in TensorFlow, including transposing matrices, performing matmul with proper shapes, and typecasting, then learn ragged tensors with varying row lengths.
Tensorflow Operations2:01
Random Values in Tensorflow6:15
Tensorflow Checkpoints: Save ML models3:34
Save and restore thousands of tensor values using TensorFlow checkpoints, tracking training progress and restoring model state when needed.
Tensorflow Lite Introduction & Advantages4:49

Training a simple regression model for mobile devices9:50
Model Testing and Conversion into Tensorflow Lite3:18
Tensorflow Lite Model Training Overview1:44
Analysing trained tflite model2:28
Creating a new Android Studio Project and GUI of Application8:28
Adding Tensorflow Lite Library In Android & Loading Tensorflow Lite Model7:45
Integrate a tflite regression model in android studio by adding an assets folder with linear.tflite, configuring a TensorFlow dependency, and loading the model with a TensorFlow Lite interpreter using ByteBuffer.
Passing Input to Tensorflow Lite Model in Android and Getting Output5:23
Using basic tflite regression model in Android overview2:08

Section Introduction2:31
Data Collection: Finding Fuel Efficiency Prediction Dataset4:56
Loading Dataset in Python for Model Training7:45
Handling missing Values in Fuel Efficiency Prediction Dataset3:24
Handling Categorical Columns in Dataset for Model Training4:38
Normalization Introduction2:12
Dataset Normalization2:40
Training Fuel Efficiency Prediction Model in Tensorflow7:04
Testing Trained Model and converting it to Tensorflow Lite Model4:20
Training Fuel Efficiency Prediction Model Overview4:30

Setting up Android Application for fuel efficiency prediction2:52
Starter Application Overview5:14
Explore the android starter code, including main activity and user interface layout, cylinder input, origin spinner with USA, Europe, and Japan, and a tf lite model setup for price prediction.
Loading Tensorflow Lite models in Android5:06
Data Normalization in Android6:28
Passing input to Tensorflow Lite model in Android and getting output4:56
Testing fuel efficiency prediction android application1:42
Fuel Efficiency Prediction Android App Overview2:40

Section Introduction1:57
Explore data collection for a house price prediction model, preprocess with normalization and one-hot encoding, train an artificial neural network, evaluate and convert to tflite for an Android app.
Getting dataset for training house price prediction model3:45
Loading dataset for training tflite model7:19
Training & Evaluating house price prediction model6:30
Retraining House Price Prediction Model4:04

Setting Up Android Studio Project2:29
What we have done so far6:02
Data Normalization in Android6:31
Normalize Android input data for house price prediction by converting inputs to floats, encoding ocean proximity categories, and applying per column mean and standard deviation to 13 features.
Passing Input to house price prediction model in Android4:42
Pass normalized 13 input features to the house price prediction model via a 2d input array and a 1x1 output array, run inference, and display the predicted price.
Testing house price prediction Android Application2:52

Requirements

Android studio installed in your PC

Description

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

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 In this course, you will learn to train your custom machine-learning models for Android and build smart Android Applications.

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

Train ML Regression Models & Build Predictive Android Apps

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Machine Learning & Deep Learning Introduction7 lectures • 33min

Python: A simple overview8 lectures • 36min

Data Science Libraries : Numpy, Pandas, Matplotlib9 lectures • 31min

Tensorflow and Tensorflow Lite7 lectures • 35min

Train a simple Regression Model and build Android Application8 lectures • 41min

Fuel Efficiency Prediction: Training an advance regression model10 lectures • 44min

Fuel Efficiency Prediction Android Application7 lectures • 29min

Training a house price prediction Model5 lectures • 24min

Building House Price Prediction Android Application5 lectures • 23min

Requirements

Description

Who this course is for: