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Machine Learning for Android Developer using Tensorflow lite

Online Courses Udemy - Machine Learning for Android Developer using Tensorflow lite, Chose your datasets, train Machine Learning models and develop Android Applications

4.3 (10 ratings), Created by Hamza Asif

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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. This course will get you started in building your FIRST deep learning model and android application using deep learning. We will learn about machine learning and deep learning and then train your first model and deploy it in android application using tenserflow lite . All the materials for this course are FREE.

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)

Predicting Fitness of a person (Classification example)

Recognizing hand written digits

Flowers Recognition Example

Stones Recognition Example

Fruits Recognition Example

We will start by learning about basics of Python  programming language. Than 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.Than 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 basic structure of neural networks.Than we will understand working of neural networks through an example.

Than we will learn about Tensorflow library and how we can use it to train Machine Learning models .After that we will look at how we can convert our model to tflite format which will be used in Android Application. 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 basics of machine learning(Deep Learning) and deploy ML models in your Android applications using Tensorflow lite

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.

Another section at the end of the course shows you how you can use dataset available in different format 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 existing android machine learning projects templates.

Suggested Prerequisites:

Basic Knowledge of Android Development

Basic Programming skills

TIPS (for getting through the course):

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

Who this course is for:

Students interested in machine learning - you'll get all the tidbits you need to add machine learning models in android

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

Machine Learning experts want to deploy their models in Android

Who this course is for:
Android Developers curious about Machine Learning
People having basic knowledge of Android Development

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