4.03 out of 5
134 reviews on Udemy

Android Machine Learning with TensorFlow lite in Java/Kotlin

Learn Machine Learning use in Android using Kotlin,Java ,Android studio and Tensorflow Lite ,Build 10+ ML Android Apps
Hamza Asif
20,046 students enrolled
Train machine learning models on datasets and developing Android Applications
Use Trained Machine Learning models inside Android Application using Android Studio
Train 10+ machine learning models and build Android Application for those models
Learn Basics of Python Programming language
Learn popular Machine Learning libraries like Numpy,Pandas and Matplotlib
Complete understanding of Machine Learning ,Deep Learning and Neural Networks
Learn basics of Tensorflow 2.0
Learn about Tensorflow Lite
Generating Tensorflow lite model from Keras model, saved model, concrete function
Train and deploy classification and regression models
Training recognition models and creating Android Applications for those models
Deploy Machine Learning models using Android Studio


  • You should have some basic knowledge of Android App Development using Java or Kotlin

Tired of traditional Android App Development courses? Now its time to learn something new and trending for Android. Machine Learning is at its peak and Android App Development is also in demand than what is better than learning both?

This course is designed for Android developers who want to learn Machine Learning and deploy machine learning models in their android apps using TensorFlow Lite. If you have very basic knowledge of Android App development and want to learn Machine Learning use in Android Applications this course is for you. This course will get you started in building your FIRST deep learning model and Android Application using both java and Kotlin Tensorflow Lite, and Android studio. We will learn about machine learning and deep learning and then train your first model and deploy it in android application using Android studio. All the materials for this course are FREE.

You can implement Application build during the apps using both java and kotlin. Separate Lectures are provided for both of these languages.

You don’t need any prior knowledge of Machine Learning to start this course. We will start by learning

  • Python Programming Language

  • Data Science Libraries

  • Basics of Machine Learning and Deep Learning

  • Tensorflow and Tensorflow Lite

Then we will train our first Machine Learning model and Develop Android Application for it using Android Studio.

The course includes examples from basic to advance

  • A very simple example

  • Example using saved model

  • Example using concrete function

  • Predicting fuel efficiency of automobiles (Regression Example)

  • Recognizing handwritten digits (Classification example)

  • Cats and Dogs classification

  • Rock Paper and Scissors Problem

  • Flowers Recognition Example

  • Stones Recognition Example

  • Fruits Recognition Example

  • Predicting Fitness of a person Practice Activity

  • Human and Horse Practice Activity

For each of these examples, we will firstly train Machine Learning model then build Android Application

We will start by learning about the basics of the Python programming language. Then we will learn about some famous Machine Learning libraries like Numpy, Matplotlib, and Pandas. After that, we will learn about Machine learning and its types. Then we look at Supervised learning in detail. We will try to understand classification and regression through examples. After we will start Deep learning. We start by looking and the basic structure of neural networks. Then we will understand the working of neural networks through an example.

Then we will learn about the Tensorflow 2.0 library and how we can use it to train Machine Learning models. After that, we will look at Tensorflow lite how we can convert our Machine Learning models to tflite format which will be used inside Android Applications. There are three ways through which you can get a tflite file

  1. From Keras Model

  2. From Concrete Function

  3. From Saved Model

We will cover all these three methods in this course.

We will learn about Feed Forwarding, Back Propagation, and activation functions through a practical example. We also look at cost function, optimizer, learning rate, Overfitting, and Dropout. We will also learn about data preprocessing techniques like One hot encoding and Data normalization.

Next, we implement a neural network using Google’s new TensorFlow library.

You should take this course If you are an Android Developer and want to learn the basics of machine learning(Deep Learning) and deploy ML models in your Android applications using Tensorflow lite and Android Studio.

This course provides you with many practical examples so that you can really see how you can train and deploy machine learning model in android. We will use Android Studio for developing Android Application for models we trained.

Another section at the end of the course shows you how you can use datasets available in different formats for a number of practical purposes.

After getting your feet wet with the fundamentals, I provide a brief overview of how you can add your machine learning model in google’s existing android machine learning project templates.

Suggested Prerequisites:

  • Basic Knowledge of Android App Development

TIPS (for getting through the course):

  • Write code yourself, don’t just sit there and look at my code.

Who this course is for:

  • Beginner Android Developers want to make their Android applications smart

  • Android Developers want to use Machine Learning in their Android Applications

  • Developers interested in the practical implementation of Machine Learning and computer vision

  • Students interested in machine learning – you’ll get all the tidbits you need to add machine learning models in android using Android studio

  • Professionals who want to use machine learning models in Android Application.

  • Machine Learning experts want to deploy their models in Android using Android studio and Tensorflow lite


Introduction and course Overview

Setting up the environment

Setting up the environment

After completing this lecture you will be able to download and install Anaconda IDE. Anaconda contain a number of software to work with python.

Installing Tensorflow

In this lecture we install Tensorflow in anaconda using anaconda prompt.We will execute command that will download Tensorflow and install it.Than we will look at Spyder that is contained inside anaconda and we write our code inside spyder.

Jupyter Notebook Introduction

Learning Python

Python Introduction and data types
Python Lists
Python List Functions
Python dictionary and tuples
Python Loops and conditional statements
Python File handling

Data Science Libraries

Numpy Introduction and arrays
Numpy functions
Numpy Operators
Pandas Introduction
Pandas reading files and handling missing values
Matplotlib introduction
Matplotlib dealing with images

Machine Learning and Deep Learning

Machine Learning, Classification and Regression

In this lecture will we look at machine learning and its types. Than we will look at classification and regression.We will try to understand difference between classification and regression problems trough examples.

Unsupervised, Reinforcement Learning
Deep Learning

In this lecture we will look at deep learning. We will look at basic structure of neural networks.

Deep Learning Part 2

In this lecture we will understand working of neural network through an example.We will see how neural network predict labels and how weights are updated while training.

Basic Concepts Part 1

In this lecture we will look at different concepts of machine learning and deep learning.

Basic Concepts Part 2


Tensorflow Introduction
Tensorflow Constants and shaping
Tensorflow rank and numpy
Tensorflow Matrix multiplication and Ragged Tensors
Tensorflow Operations
Generating Random Values
Saving Variables using Checkpoints

Training First model and creating Android Application

Creating and training first ML model

In this lecture we will train our first deep learning model.Firstly we will create out own dataset that contain certain pattern. You will see that how our model will pattern in our data.Than we will  save our model in a tflite format which will be used in android application.

Creating Android Application for the model Java

In this lecture we will create our first android application.We will add our model inside asset folder than we will use this model to predict label.

Creating Android Application for the model Kotlin

Concrete function and Saved model examples

Concrete Function Example
Saved Model Example

Predicting Fuel Efficiency of automobiles

Loading data and preprocessing

In this lecture we will work on a real world regression problem .We will learn how to download dataset using from internet and than how to preprocess data for model training.

One Hot Encoding

In this lecture we will look at one of important step of data preprocessing known as one hot encoding.You will learn that how to handle categorical data while model training.

Normalizing data and training model

In this lecture you will learn that how can you normalize you data before passing that data for model training.Than we will create our model that contain neural network.Than we will train our  model on that data.

Fuel Efficiency Application Part 1

In this lecture we will create android application for the model we trained in the previous lecture.We will add our model in android studio project.

Java: Fuel Efficiency Application Part 2

In this lecture we complete coding of application we started in the previous lecture. You will learn how to pass input to model and get output from that model in java.

Kotlin: Fuel Efficiency Application
Testing Application

Recognizing Handwritten digits

Loading the dataset

In this lecture you will learn that how you can use keras dataset for model training.We will use keras digit recognition data set.So we will load this data set using pandas.

Matplotlib and normalizing data

In this lecture you will learn how to plot images using matplotlib as we are working with images in this classification example.Then we will normalize our dataset that contain images of size 28*28 pixels.

Training model

In this lecture you will learn that how you can add dropout while creating your learning model.We will create our model and train it on our training dataset.

Evaluating model and creating tflite file

In this lecture we will evaluate the model we trained in the previous lecture.After testing our model we will save our model in a tflite format.

Digit Recognizer Application 1

In this lecture you will learn about finger paint view we will be using in our application to draw and capture and written digits.

Digit Recognizer Application Part 2

In this lecture we will look at the code present in Result class.This class is responsible for extracting our output.

Digit Recognizer Application Part 3

In this lecture we will display result that model predicted in the respected textviews. We use Result class for that purpose.

Testing Application

In this lecture we will test application we created. We will draw different digits with our finger in finger paint view and see what our model thinks that these digits are.

Kotlin: Digit Recognizer Android Application

Recognition Section

Transfer Learning
Google Colab
Flower Recognition loading data set
Flower Recognition Training and evaluating model
Flower Recognition Detailed Process
Flower Recognition model
Evaluating tflite model

Cats and Dogs Classification

Train cats and dogs model

In this lecture, we will train machine learning model on cats and dogs dataset. We will use transfer learning and retrain ImageNetV2 model on our dataset.

Java: Build Cats and dogs classification Application

In this lecture, we will use cats and dogs model that we trained in the previous lecture inside Android Application.

Kotlin: Build Cats and dogs classification Application

Rock Paper and Scissors Problem

Training the model
Java: Rock Paper and Scissor Android Application
Kotlin: Rock Paper and Scissor Android Application

Practice Activity 1 Predict Fitness of a Person


This lecture contains introduction of practice section and structure of practice activity.For each practice activity initial code will be provided to you and your task is to complete the code and get required accuracy of the model

Practice Activity 1 Part 1
Practice Activity 1 Part 2
Practice Activity 1 Part 3
Practice Activity 1 Part 4
Practice Activity 1 Solution
Practice Activity 1 Application 1
Practice Activity 1 Application 2

Practice Activity 2 Human and Horses

Training Human and Horses model
Java: Build Human and Horses classification Application
Kotlin: Build Human and Horses classification Application


Working with images Part 1

In this lecture we will look at kaggle.com and how we can download datasets from it.

Working with images Part 2

In this lecture we will write code to load images we downloaded in previous lecture.We will see how we can work with real world images dataset

Working with CSV

In this lecture we will see how we can work with datasets available in format.

You can view and review the lecture materials indefinitely, like an on-demand channel.
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