Build 10+ Machine Learning Powered Android Apps | Train ML Models for Android | Use ML Models in Android App Development

SUMMARY

This course is a comprehensive guide for Android developers looking to integrate machine learning into their applications using TensorFlow Lite. It covers the entire process from training machine learning models to deploying them in Android apps, utilizing both Java and Kotlin. As machine learning continues to gain traction in the tech industry, this course provides a unique opportunity to blend Android app development with cutting-edge AI techniques.

Course Objectives

Participants will learn how to:

  • Train and deploy over ten different machine learning models.
  • Implement classification and regression models in Android.
  • Convert models to TensorFlow Lite format for use in mobile applications.
  • Create Android applications for various machine learning tasks, such as image recognition and fitness prediction.

Course Structure

  1. Introduction to Machine Learning:
    • The course starts with fundamental concepts in Python programming, essential data science libraries (like NumPy, Matplotlib, and Pandas), and an introduction to machine learning and deep learning.
  2. Basic Concepts:
    • Key concepts such as supervised learning, classification, and regression are introduced. Participants will engage with practical examples, enhancing their understanding of these principles.
  3. Deep Learning Fundamentals:
    • An overview of neural networks, including their structure and function, is provided. Topics include feedforward and backpropagation, activation functions, cost functions, optimizers, learning rates, overfitting, and dropout techniques.
  4. TensorFlow and TensorFlow Lite:
    • The course covers how to use TensorFlow 2.0 for training machine learning models and explains the process of converting models to TensorFlow Lite (tflite) format. Different methods for obtaining tflite files are discussed, including:
      • From Keras models
      • From saved models
      • From concrete functions
  5. Hands-On Projects:
    • Throughout the course, participants will work on practical projects to apply their skills. Some of the highlighted projects include:
      • Cats and Dogs Classification: Developing an application to classify images of cats and dogs.
      • Rock-Paper-Scissors Game: Building a simple interactive game that uses image recognition.
      • Flowers Recognition: An application that identifies different types of flowers.
      • Precious Stones Recognition: A project focused on recognizing various gemstones.
      • Fruits Recognition: Developing an app to classify different fruits.
      • Predicting Fitness Levels: An application that assesses a person’s fitness based on input data.
      • Human and Horse Image Classification: A project to classify images based on the subject.
  6. Data Handling:
    • The course also addresses data preprocessing techniques essential for machine learning, such as one-hot encoding and data normalization. Participants will learn how to effectively handle datasets in various formats for their projects.
  7. Integration with Android Studio:
    • Using Android Studio, learners will integrate their trained models into functional Android applications. Separate lectures are provided for implementing solutions in both Java and Kotlin, catering to developers with varying language preferences.
  8. Adding Models to Existing Projects:
    • Towards the end of the course, an overview of how to incorporate machine learning models into existing Android projects using Google’s machine-learning templates is provided. This equips participants with the skills to enhance existing applications with AI capabilities.

Target Audience

This course is ideal for:

  • Beginner Android Developers: Those who want to enhance their applications by integrating machine learning.
  • Existing Android Developers: Individuals looking to add machine learning capabilities to their current skill set.
  • Developers Interested in Practical Applications: Those wanting to understand the implementation of machine learning and computer vision.
  • Students and Professionals: Individuals seeking knowledge in machine learning with practical applications in Android development.
  • Machine Learning Experts: Those looking to deploy their existing models in Android applications.

Requirements

No prior knowledge of machine learning is necessary to enroll in this course, making it accessible for beginners. A basic understanding of Android development is beneficial, but the course provides all the foundational information needed to succeed.

 

What you’ll learn
  • Train machine learning models for Android Applications
  • Use of Tensorflow Lite Models inside Android Applications using both Java and Kotlin
  • Use Trained Machine Learning models inside Android Application using Android Studio
  • Train 10+ machine learning models and build Android Applications for those models
  • Train and deploy classification and regression models in Android
  • Generating Tensorflow lite model from Keras model, saved model, concrete function
  • Training image recognition models and creating Android Applications for those models
  • Build a Cats and Dogs classification Android Application
  • Rock Paper and Scissors Problem in Android
  • Flowers Recognition Android Application
  • Android Application to Recognize Precious Stones
  • Fruits Recognition Android Application
  • Android Application to Predict Fitness of a Person
  • Human & Horse Problem in Android
Description

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 so 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 an Android application using Android Studio. All the materials for this course are FREE.

You can follow this course 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 using Android Studio.

The course includes examples from basic to advanced

  • A very simple Machine Learning example
  • 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 the Fitness of a Person Practice Activity
  • Human and Horse Practice Activity

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 and 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 learn how you can train and deploy machine learning models in Android. We will use Android Studio to develop Android Applications for the 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.

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 Applications.
  • Machine Learning experts want to deploy their models in Android using Android Studio and Tensorflow Lite

 

 

 

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