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Android & ML 2025 - Train Tensorflow Lite Models for Android

Name: Android & ML 2025 - Train Tensorflow Lite Models for Android
Rating: 4.4 (21 reviews)

Train Image Classification, Object Detection and Regression models for Android - Build Smart Android Kotlin Applications

Created byMobile ML Academy by Hamza Asif

Last updated 9/2025

English

What you'll learn

Train Machine Learning models for Android Applications
Train Image Classification and Object Detection Models for Android Apps
Train Linear Regression Models for Android Apps
Integrate Tensorflow Lite models in Android kotlin Apps
Use Computer Vision Models in Android with both Images and Live Camera Footage
Train Object Detection model to count and detect fruits and build Android Application
Train a fruit classification model and build a Fruit Recognition Android Application
Train a brain tumor classification model and build Android App
Train a machine learning model and build a fuel efficiency prediction Android Application
Train a machine learning model and build a house price prediction Android Application
Train Any Prediction, Classification & Object Detection Model & use it in Android Applications
Analysing & using advance regression models in Android Applications
Data Collection, Data Annotation & Preprocessing for ML model training for Android Application
Basics of Machine Learning & Deep Learning for training Machine learning Models for Android
Understand the working of artificial neural networks for training machine learning for Android
Basic syntax of Python programming language to train ML models for Android
Use of data science libraries like numpy, pandas and matplotlib

Course content

23 sections • 141 lectures • 11h 56m total length

What is Machine Learning3:15
Supervised Machine Learning3:33
Regression and Classification2:08
Unsupervised Machine Learning & Reinforcement Learning3:19
Deep Learning and Neural Network Introduction5:56
Neural Network Example10:05
Train a neural network to predict house price from area and rooms using a dataset, adjusting weights via feed forwarding and back propagation across epochs.
Working of Neural Networks for Image Classification4:51
Convert 28x28 images to 1D 784-element arrays and feed them to the input. Train the network with back propagation to update weights and identify German Shepherd, Afghan Hound, and Pointer.
Basic Deep Learning Concepts4:49
Explore the basics of machine learning, including supervised and unsupervised learning, then dive into deep learning with neural networks, covering feed forwarding, back propagation, learning rate, loss function, and dropout.

Google Colab Introduction6:43
Explore Google Colab to run Python code in your browser with zero configuration, access free GPUs, and train machine learning models for mobile applications.
Python Introduction & data types4:05
Learn Python basics, including variables and data types, and explore how to use Google Colab notebooks to train machine learning models for Android apps.
Python Numbers2:36
Explore Python numbers by creating integer, long, float, and complex variables, deleting variables with del, and inspecting types to understand basic number types in Python.
Python Strings2:49
Python Lists6:50
Learn how Python lists serve as a data type that can hold mixed items, access by index or slice, and modify with methods like append, insert, remove, sort, and reverse.
Python dictionary & tuples3:11
Explore Python tuples and dictionaries, compare tuples to lists by their parentheses and immutability, and learn to create them and access elements via index or keys.
Python loops & conditional statements4:40
File handling in Python4:44
Learn to read and write files in Python, save models to tflite format, and open files with modes such as r, w, wb, rb, and a.

Numpy Introduction5:52
Explore numpy basics for Python, including creating multi-dimensional arrays, inspecting shape and size, and specifying data types, with hands-on Colab notebook demonstrations.
Numpy Functions and Generating Random Values3:21
Numpy Operators2:12
Explore NumPy operators, starting with unary operators to compute min, max, and sum, then demonstrate binary operators for element-wise operations and the dot product.
Matrix Multiplications and Sorting in Numpy2:08
Pandas Introduction4:32
Loading CSV in pandas2:14
Learn to load a csv into a pandas data frame with read_csv, upload the file, and inspect the nba dataset for ML model training.
Handling Missing values in dataset with pandas3:29
Identify missing values in a pandas data frame with isnull, and fill or drop them while checking the data frame shape.
Matplotlib & charts in python3:57
Dealing images with Matplotlib3:01
Explore how to load and display images in Python notebooks with matplotlib, using imread and plt.imshow, and observe images as numpy arrays with their shapes for data visualization.

Tensorflow Introduction | Variables & Constants6:29
Learn how to use TensorFlow and TensorFlow Lite to declare variables and constants, create tensors and arrays, assign new values, and specify dtypes for mobile ML models.
Shapes & Ranks of Tensors6:50
Shape and reshape TensorFlow Lite tensors with tf.reshape, inspect shapes via the shape property, then determine rank with tf.rank and perform element-wise operations on two tensors.
Matrix Multiplication & Ragged Tensors4:51
Explore matrix multiplication in TensorFlow using tf.matmul with a transposed second matrix, and learn ragged tensors and typecasting with tf.cast and dtype handling.
Tensorflow Operations2:01
Learn how to compute means in TensorFlow Lite using tf.reduce_mean, including scalar, 1d, and 2d tensors, and how axis parameters determine column-wise and row-wise averages.
Generating Random Values in Tensorflow6:15
Generate random tensors in TensorFlow using normal and uniform distributions, with controlled shape, mean, stddev, and data type; set seeds for repeatable results and explore concatenation and argmax/argmin.
Tensorflow Checkpoints3:34
Tensorflow Lite Introduction & Advantages4:49

Train a simple regression model for Android9:50
Train a simple regression model and convert it to TensorFlow Lite for Android, using TensorFlow, Keras, and NumPy in Google Colab to prepare data and train with SGD.
Testing model and converting it to a tflite(Tensorflow lite) format for Android3:18
Test trained model with inputs to verify outputs match y equals 2x minus 1, then convert it to TensorFlow Lite for Android with a Keras model and tf lite converter.
Model training for Android app development overview1:44
Creating a new Android Studio Project and GUI of Application8:29
Create a new Android Studio project in Kotlin, design a GUI with a text field, a button, and a result view, and wire input to the ML model for prediction.
Adding Tensorflow Lite Library In Android & Loading Tensorflow Lite Model8:00
Passing Input to Tensorflow Lite Model in Android and Getting Output7:24

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
Identify missing values in a fuel efficiency dataset for Android ML using is.na to count them. Resolve gaps with dropna or zero-fill, ensuring clean data before training TensorFlow Lite models.
Handling Categorical Columns in Dataset for Model Training4:38
Training and testing datasets4:19
Normalization Introduction2:12
Normalization brings numeric features to a common scale before model training. It divides values like area and rooms by their maxima to balance influence, and notes there are normalization types.
Dataset Normalization2:40
Training Fuel Efficiency Prediction Model in Tensorflow7:04
Normalize the training and testing data, build a multi-layer neural network with tf.keras sequential, and train it to monitor loss using MSE and MAE across epochs.
Testing Trained Model and converting it to Tensorflow Lite Model4:20
Test a trained model on a 20% test set, compute mean absolute error and mean square error, compare predictions to actuals, then convert to a TensorFlow Lite model for Android.
Training Fuel Efficiency Prediction Model Overview4:30

Setting up Android Application for fuel efficiency prediction7:19
Loading Tensorflow Lite models & performaning normalization in Android8:38
Load and run a TensorFlow Lite fuel-efficiency model in an Android app using a TensorFlow Lite interpreter, region inputs (USA, Europe, Japan), and mean and standard deviation normalization.
Passing input to Tensorflow Lite model in Android and getting output3:40
Pass input to a TensorFlow Lite model in Android by building a two-dimensional input array of nine features, running inference, and displaying the predicted miles per gallon.
Testing fuel efficiency prediction android application1:54

Section Introduction1:57
Getting dataset for training house price prediction model3:45
Loading dataset for training tflite model7:19
Load and prepare a housing dataset in Colab to train a house price model with pandas and TensorFlow. Learn to handle missing values and one-hot encode categories.
Training & Evaluating house price prediction model6:30
Retraining House Price Prediction Model4:04
Retrain the house price prediction model by adding neural network layers and neurons, compare training vs testing results, identify overfitting, and select the best architecture for TensorFlow Lite on Android.
House Price Prediction Android App7:28
Test the Android App2:22
Test the Android app in the simulator by entering a row's values and predicting house price, then compare the predicted value to the actual value from the test dataset.

Data Collection Introduction3:25
Learn how to collect image datasets for training Android machine learning models, including existing datasets, web scraping, manual collection, and data augmentation, with per-class image targets and size considerations.
Finding ready to use dataset for training image classification models5:13
Exploring Downloaded dataset for training custom image classification models3:05
Unzip the downloaded dataset, select six classes (apple, banana, watermelon, onion, lemon, potato), organize them into a dataset folder, and zip it to train a TensorFlow Lite image classifier.

Requirements

Visual Studio Code or Android Installed on Your System

Description

Do you want to train different Machine Learning models and build smart Android applications in 2025 then Welcome to this course.

In this course, you will learn to train powerful

Image Classification
Object Detection
Linear Regression

model in python from scratch. After that you will learn to

Use your custom trained Machine Learning Models in Android
Use existing tensorflow lite models in Android Apps

Regression

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 Inside this course, you will learn to train your custom linear regression models in Tensorflow Lite format and build smart Android Applications.

Image Classification & Applications

Image classification is the process of recognizing different entities or things in an image or video. You can recognize animals, plants, diseases, food, activities, colors, things, fictional characters, drinks, etc with image recognition.

In e-commerce applications image classification can be used to categorize products based on their visual features, So it is used to organize products into categories for easy browsing.
Image classification can be used to power visual search in mobile apps, so users can take a picture of an object and then find similar items for sale.
Image classification can be used in medical apps to diagnose disease based on medical images, such as X-rays or CT scans.
We can use image classification to build countless recognition applications for performing number of tasks, like we can train a model and build applications to recognize
- Different Breeds of dogs
- Different Types of plants
- Different Species of Animals
- Different kind of precious stones

Image Classification & Applications

Object detection is a powerful computer vision technique that can accurately identify and pinpoint the location of various objects within images or videos. By recognizing objects like cars, people, and animals, this technology empowers applications such as security surveillance, autonomous vehicles, and smartphone apps that can identify objects through the camera lens.

Key Applications:

Autonomous Vehicles: Cars equipped with object detection can safely navigate roads, avoid collisions, and enhance driver assistance systems.
Surveillance Systems: Security cameras can identify individuals, track suspicious activity, and detect intrusions.
Retail: Stores can monitor customer behavior, manage inventory, and prevent theft.
Healthcare: Medical imaging systems can detect anomalies like tumors and fractures.
Agriculture: Farmers can monitor crops, livestock, and detect pests or diseases.
Manufacturing: Quality control and automation can be improved through object inspection and robotic guidance.
Sports Analytics: Tracking player movements and equipment can enhance performance analysis and fan experience.
Environmental Monitoring: Wildlife conservation and habitat protection can benefit from object detection.
Smart Cities: Traffic management, public space monitoring, and waste management can be optimized.

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 dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for 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 machine learning 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

Regression Models Training

Training Your First Machine Learning Model:
- Harness TensorFlow and Python to create a simple linear regression model
- Convert the model into TFLite format, making it compatible with Android
- Learn to integrate the tflite model into Android apps for Android
Fuel Efficiency Prediction:
- Apply your knowledge to a real-world problem by predicting automobile fuel efficiency
- Seamlessly integrate the model into a Android app for an intuitive fuel efficiency prediction experience
House Price Prediction in Android:
- Master the art of training machine learning models on substantial datasets
- Utilize the trained model within your Android app to predict house prices confidently

Computer Vision Model Training

Image Classification in Android:
- Collect and process dataset for model training
- Train image classification models on custom datasets with Teachable Machine
- Train image classification models on custom datasets with Transfer Learning
- Use image classification models in Android with both images and live camera footage
Object Detection in Android
- Collect and Annotate Dataset for Object Detection Model Training
- Train Object Detection Models
- Use object detection models in Android with Images & Videos

The Android Advantage: By the end of this course, you'll be equipped to:

Train advanced machine learning models for accurate predictions
Seamlessly integrate tflite models into your Android applications
Analyze and use existing regression & vision (ML) models effectively within the Android ecosystem

Who Should Enroll:

Aspiring Android developers eager to add predictive modeling to their skillset
Beginner Android developer with very little knowledge of mobile app development
Intermediate Android developer wanted to build a powerful Machine Learning-based application
Experienced Android developers wanted to use Machine Learning models inside their applications.

Step into the World of Android and Machine Learning: Join us on this exciting journey and unlock the potential of Android and Machine Learning. 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 Machine Learning!

Who this course is for:

Beginner Android Developers who want to train ML models and build Machine Learning based Android Applications
Aspiring Android developers eager to add ML 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

Android & ML 2025 - Train Tensorflow Lite Models for Android

What you'll learn

Explore related topics

Course content

Machine Learning & Deep Learning for Android App Development8 lectures • 38min

Python Programming Language Short Course8 lectures • 36min

Data Science Libraries for Android9 lectures • 31min

Tensorflow & Tensorflow Lite for Android7 lectures • 35min

Training a basic regression model for Android6 lectures • 39min

Fuel Efficiency Prediction: Training an advance regression model11 lectures • 48min

Fuel Efficiency Prediction Android Application4 lectures • 22min

Training a House Price Prediction Model & Building Android App7 lectures • 33min

Image Classification1 lecture • 5min

Data Collection - Collecting Dataset for Training Image Classification Model3 lectures • 12min

Requirements

Description

Who this course is for: