Android Machine Learning with TensorFlow lite in Java/Kotlin

Learn Machine Learning use in Android with Tensorflow Lite , Build 10+ Smart 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



Requirements
Basic knowledge of Android Development

Description

Requirements
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 developer
s 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
From Keras Model
From Concrete Function
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
Who this course is for:
Android Developers curious about Machine Learning
People having basic knowledge of Android Development
People want to make their Android Applications smart

More Info