Android Machine Learning with TensorFlow lite in JavaKotlin

Android Machine Learning with TensorFlow lite in Java/Kotlin, available at $49.99, has an average rating of 3.45, with 130 lectures, based on 247 reviews, and has 24433 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner Android Developer curious about Machine learning and computer vision use in Android using Java or Kotlin or Experienced Android Professional want to add Machine Learning models in their Android Applications or Intermediate Android developers looking to enhance their skillset or App developer want to learn use of Machine learning in their Android Applications It is particularly useful for Beginner Android Developer curious about Machine learning and computer vision use in Android using Java or Kotlin or Experienced Android Professional want to add Machine Learning models in their Android Applications or Intermediate Android developers looking to enhance their skillset or App developer want to learn use of Machine learning in their Android Applications.

Enroll now: Android Machine Learning with TensorFlow lite in Java/Kotlin

Summary

Title: Android Machine Learning with TensorFlow lite in Java/Kotlin

Price: $49.99

Average Rating: 3.45

Number of Lectures: 130

Number of Published Lectures: 90

Number of Curriculum Items: 130

Number of Published Curriculum Objects: 90

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction and course Overview

Chapter 2: Machine Learning & Deep Learning

Lecture 1: Machine Learning Introduction

Lecture 2: Supervised Machine Learning: Regression & Classification

Lecture 3: Unsupervised Machine Learning & Reinforcement Learning

Lecture 4: Deep Learning and regression models training

Lecture 5: Basic Deep Learning Concepts

Chapter 3: Python

Lecture 1: Google Colab Introduction

Lecture 2: Python Introduction & data types

Lecture 3: Python Lists

Lecture 4: Python dictionary & tuples

Lecture 5: Python loops & conditional statements

Lecture 6: File handling in Python

Chapter 4: Data Science Libraries

Lecture 1: Numpy Introduction

Lecture 2: Numpy Operations

Lecture 3: Numpy Functions

Lecture 4: Pandas Introduction

Lecture 5: Loading CSV in pandas

Lecture 6: Handling Missing values in dataset with pandas

Lecture 7: Matplotlib & charts in python

Lecture 8: Dealing images with Matplotlib

Chapter 5: Tensorflow & Tensorflow Lite

Lecture 1: Tensorflow Introduction | Variables & Constants

Lecture 2: Shapes & Ranks of Tensors

Lecture 3: Matrix Multiplication & Ragged Tensors

Lecture 4: Tensorflow Operations

Lecture 5: Generating Random Values in Tensorflow

Lecture 6: Tensorflow Checkpoints

Chapter 6: Basic Regression Example

Lecture 1: Section Introduction

Lecture 2: Training a basic regression model for Android

Lecture 3: Testing our trained model and converting it to tensorflow lite format

Lecture 4: Training a basic regression model overview

Lecture 5: Analysing trained tensorflow lite models

Lecture 6: Creating new android project and building GUI of application

Lecture 7: Adding tensorflow lite library in Android and loading tflite model

Lecture 8: Passing input to tflite model and getting output in Android

Lecture 9: Using basic tflite model in Android overview

Chapter 7: Training Fuel Efficiency Prediction Model and Building Android Application

Lecture 1: Getting dataset for training tensorflow lite models

Lecture 2: Loading dataset for training regression models

Lecture 3: Handling missing values in dataset

Lecture 4: Handling categorical columns in dataset

Lecture 5: Dividing dataset into training and testing

Lecture 6: Dataset normalization

Lecture 7: Training fuel efficiency prediction tensorflow lite model

Lecture 8: Testing model and converting it tensorflow lite format

Lecture 9: Fuel Efficiency prediction model training overview

Lecture 10: Setting up Android project for fuel efficiency prediction application

Lecture 11: What we have done so far

Lecture 12: Loading tensorflow lite model in Android and preparing input for model

Lecture 13: Normalizing data in Android for model input

Lecture 14: Passing input to tflite model in Android and getting output

Lecture 15: Testing fuel efficiency prediction android application

Lecture 16: Fuel Efficiency Prediction Android App Overview

Chapter 8: Concrete function and Saved model examples

Lecture 1: Android Tensorflow lite Concrete Function Example

Lecture 2: Android Tensorflow lite Saved Model Example

Chapter 9: Handwritten digits recognition application

Lecture 1: Android ML: Loading the dataset

Lecture 2: Android ML: Matplotlib and normalizing data

Lecture 3: Android ML: Training digit recognition model

Lecture 4: Android ML: Evaluating model and creating tflite file

Lecture 5: Android ML: Digit Recognizer Application 1

Lecture 6: Android ML: Digit Recognizer Application Part 2

Lecture 7: Android ML: Digit Recognizer Application Part 3

Lecture 8: Android ML: Testing digit recognition Application

Lecture 9: Kotlin: Digit Recognizer Android Application

Chapter 10: Recognition Section

Lecture 1: Android ML: Transfer Learning

Lecture 2: Android ML: Google Colab

Lecture 3: Android ML: Flower Recognition loading data set

Lecture 4: Android ML: Flower Recognition Training and evaluating model

Lecture 5: Android ML: Flower Recognition Detailed Process

Lecture 6: Android ML: Flower Recognition model

Lecture 7: Android ML: Evaluating tflite model

Chapter 11: Cats and Dogs Classification

Lecture 1: Android ML: Train cats and dogs model

Lecture 2: Android ML Java: Build Cats and dogs classification Application

Lecture 3: Android ML Kotlin: Build Cats and dogs classification Application

Chapter 12: Rock Paper and Scissors Problem

Lecture 1: Android ML: Training rock paper scissors model

Lecture 2: Android ML Java: Rock Paper and Scissor Android Application

Lecture 3: Android ML Kotlin: Rock Paper and Scissor Android Application

Chapter 13: Practice Activity 1 Predict Fitness of a Person

Lecture 1: Android ML: Introduction

Lecture 2: Android ML: Fitness Practice Activity 1 Part 1

Lecture 3: Android ML: Fitness Practice Activity 1 Part 2

Lecture 4: Android ML: Fitness Practice Activity 1 Part 3

Lecture 5: Android ML: Fitness Practice Activity 1 Part 4

Lecture 6: Android ML: Fitness Practice Activity 1 Solution

Lecture 7: Android ML: Fitness Practice Activity 1 Application 1

Lecture 8: Android ML: Fitness Practice Activity 1 Application 2

Chapter 14: Practice Activity 2 Human and Horses

Lecture 1: Android ML: Human and horses Assignment

Lecture 2: Android ML: Training Human and Horses model

Lecture 3: Android ML Java: Build Human and Horses classification Application

Instructors

Mobile ML Academy by Hamza Asif
ML & AI based Flutter, Android, IOS & React Native Courses

Rating Distribution

1 stars: 21 votes

2 stars: 18 votes

3 stars: 41 votes

4 stars: 86 votes

5 stars: 81 votes

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