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Android Machine Learning with TensorFlow lite in Java/Kotlin

Name: Android Machine Learning with TensorFlow lite in Java/Kotlin
Rating: 3.8 (255 reviews)

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

Created byMobile ML Academy by Hamza Asif

Last updated 2/2024

English

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

Course content

15 sections • 90 lectures • 7h 7m total length

Introduction and course Overview2:32

Machine Learning Introduction3:25
Learn how machine learning uses data and algorithms to find patterns, with data sets, training data, testing data, features and labels, and supervised, unsupervised, and reinforcement learning.
Supervised Machine Learning: Regression & Classification4:42
Unsupervised Machine Learning & Reinforcement Learning3:21
Deep Learning and regression models training13:04
Explore deep learning with artificial neural networks, learning how layered neurons perform feedforward and backpropagation to train models for regression and classification.
Basic Deep Learning Concepts6:13
Explore core machine learning concepts, including supervised and unsupervised learning, deep learning with neural networks, and key training processes like feed forwarding, back propagation, learning rate, and loss functions.

Google Colab Introduction4:58
Python Introduction & data types8:34
Python Lists6:03
Python dictionary & tuples3:40
Python loops & conditional statements3:58
Explore Python conditional statements with if, elif, and else to compare two values, and practice while and for loops to iterate lists and strings, plus handling user input.
File handling in Python4:21
Open and close files in Python, use read and write modes (r, w, wb, a), and create or verify the model file during ML training.

Numpy Introduction5:22
Numpy Operations4:33
Numpy Functions4:41
Explore numpy functions by applying unary and binary operators to arrays, perform dot products and matrix multiplication with np.dot and np.matmul, and sort multidimensional arrays by axis.
Pandas Introduction3:29
Discover the pandas data science library for manipulation in Python, and learn to load data into data frames, create frames from lists and dictionaries, and work with rows and columns.
Loading CSV in pandas3:13
Handling Missing values in dataset with pandas3:41
Learn how to load a csv into a pandas data frame, detect missing values with isnull, fill or drop them with fillna and dropna, and inspect shape for model training.
Matplotlib & charts in python3:21
Dealing images with Matplotlib2:40

Section Introduction2:45
Train a simple linear regression model to uncover the pattern output equals two times input minus one from a two-column dataset, then test it in a Flutter app.
Training a basic regression model for Android9:56
Testing our trained model and converting it to tensorflow lite format3:26
Training a basic regression model overview2:00
Review training a first linear regression model with TensorFlow, using a simple one-hidden-layer, one-neuron network, and converting it to a tflite file for mobile applications.
Analysing trained tensorflow lite models2:28
Analyze the tflite model to inspect input and output shapes using natron, revealing a single input value and a single output value with float 32 for mobile app integration.
Creating new android project and building GUI of application8:28
Adding tensorflow lite library in Android and loading tflite model7:45
Passing input to tflite model and getting output in Android5:23
Using basic tflite model in Android overview2:08

Getting dataset for training tensorflow lite models4:56
Train your first real world regression model to predict mpg (fuel efficiency) for vehicles using features like cylinders, displacement, weight, and horsepower from the auto mpg dataset on Kaggle.
Loading dataset for training regression models7:45
Handling missing values in dataset3:24
Handling categorical columns in dataset4:38
Dividing dataset into training and testing4:19
Dataset normalization2:40
Normalize dataset features by subtracting the mean and dividing by the standard deviation for each column, applying this to training and test data before model training.
Training fuel efficiency prediction tensorflow lite model7:04
Normalize training and test data, then build and train a Keras sequential neural network in TensorFlow Lite for fuel efficiency prediction.
Testing model and converting it tensorflow lite format4:20
Fuel Efficiency prediction model training overview4:30
Train a real-world regression model for fuel efficiency using a Kaggle dataset in Google Colab, apply one-hot encoding and normalization, split 80/20, and convert to tflite for Android apps.
Setting up Android project for fuel efficiency prediction application2:52
What we have done so far5:14
Loading tensorflow lite model in Android and preparing input for model5:06
Load a TensorFlow Lite model in an Android app with a mapped ByteBuffer and interpreter, then prepare and normalize UI inputs (cylinders, displacement, horsepower, weight, acceleration, year, region one-hot) for inference.
Normalizing data in Android for model input6:28
Passing input to tflite model in Android and getting output4:56
Testing fuel efficiency prediction android application1:42
Test the fuel efficiency prediction Android app in the simulator, comparing the predicted 15.02 with the actual 15 using cylinders, displacement, horsepower, weight, acceleration, model year, and origin USA.
Fuel Efficiency Prediction Android App Overview2:40

Android ML: Loading the dataset2:23
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.
Android ML: Matplotlib and normalizing data1:46
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.
Android ML: Training digit recognition model3:11
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.
Android ML: Evaluating model and creating tflite file2:01
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.
Android ML: Digit Recognizer Application 18:24
In this lecture you will learn about finger paint view we will be using in our application to draw and capture and written digits.
Android ML: Digit Recognizer Application Part 21:52
In this lecture we will look at the code present in Result class.This class is responsible for extracting our output.
Android ML: Digit Recognizer Application Part 34:49
In this lecture we will display result that model predicted in the respected textviews. We use Result class for that purpose.
Android ML: Testing digit recognition Application1:57
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 Application9:01

Android ML: Transfer Learning2:05
Explore transfer learning in android machine learning with TensorFlow Lite by using a model trained on vegetables as a starting point to classify flowers, boosting performance.
Android ML: Google Colab3:07
Android ML: Flower Recognition loading data set4:20
Android ML: Flower Recognition Training and evaluating model4:14
Android ML: Flower Recognition Detailed Process7:48
Build an Android flower recognition model with TensorFlow Lite in Java/Kotlin using transfer learning, loading a labeled dataset, training with validation, evaluating with test data, and exporting a tflite model.
Android ML: Flower Recognition model3:44
Android ML: Evaluating tflite model3:15

Requirements

A very little knowledge of Android Development

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

For each of these examples, we will first train the machine-learning model and then build an 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 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

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 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

Who this course is for:

Beginner Android Developer curious about Machine learning and computer vision use in Android using Java or Kotlin
Experienced Android Professional want to add Machine Learning models in their Android Applications
Intermediate Android developers looking to enhance their skillset
App developer want to learn use of Machine learning in their Android Applications

Android Machine Learning with TensorFlow lite in Java/Kotlin

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Machine Learning & Deep Learning5 lectures • 31min

Python6 lectures • 32min

Data Science Libraries8 lectures • 31min

Tensorflow & Tensorflow Lite6 lectures • 29min

Basic Regression Example9 lectures • 44min

Training Fuel Efficiency Prediction Model and Building Android Application16 lectures • 1hr 13min

Concrete function and Saved model examples2 lectures • 6min

Handwritten digits recognition application9 lectures • 35min

Recognition Section7 lectures • 29min

Requirements

Description

Who this course is for: