Android Machine Learning with TensorFlow lite in Java/Kotlin

Learn Machine Learning used in Android using Kotlin Java Android studio and Tensorflow Lite, Build 10+ ML Android Apps

What you’ll learn

  • 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


  • Basic knowledge of Android Development


  • You should have some basic knowledge of Android App Development using Java or Kotlin
Tired of traditional Android App Development courses? Now it’s 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 used 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 applications 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 an 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 the 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 first train the 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. The From Saved Model

We will cover all these three methods in this course.

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 models in android. We will use Android Studio for developing Android Applications 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 to 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

Who this course is for:

  • Android Developers curious about Machine Learning
  • People having a basic knowledge of Android Development
  • People want to make their Android Applications smart

Last updated: 1/2021 | Size: 3 GB
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