Free Udemy Courses

Android Machine Learning with TensorFlow lite in Java/Kotlin

PUBLISHER :- Hamza Asif

LANGUAGE :- English

PRIZE :- 117.46$ 0$

Android Machine Learning with TensorFlow lite in Java/Kotlin


  • Android Developers curious about Machine Learning
  • People having basic knowledge of Android Development
  • People want to make their Android Applications smart


  • Basic knowledge of Android Development


  • Train AI models on datasets and creating Android Applications
  • Utilize Trained Machine Learning models inside Android Application utilizing Android Studio
  • Train 10+ AI models and fabricate 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 of models and creating the Awesome Android Applications for those models
  • Deploy Machine Learning models using Android Studio



  • You ought to have some essential information on Android App Development utilizing Java or Kotlin

Worn out on customary Android App Development courses? Presently its opportunity to gain some new useful knowledge and moving for Android. AI is at its pinnacle and Android App Development is additionally sought after than what is superior to realizing both?

This course is intended for Android engineers who need to learn Machine Learning and send AI models in their android applications utilizing TensorFlow Lite. On the off chance that you have extremely essential information on Android App improvement and need to learn Machine Learning use in Android Applications this course is for you. This course will kick you off in building your FIRST profound learning model and Android Application utilizing both java and Kotlin Tensorflow Lite, and Android studio. We will find out about AI and profound learning and afterward train your first model and convey it in android application utilizing Android studio. All the materials for this course are FREE.

You can actualize Application work during the applications utilizing both java and kotlin. Separate Lectures are given to both of these dialects.

You needn’t bother with any earlier information on Machine Learning to begin this course. We will begin by learning

  • Python Programming Language
  • Information Science Libraries
  • Rudiments of Machine Learning and Deep Learning
  • Tensorflow and Tensorflow Lite

At that point we will prepare our first Machine Learning display and Develop Android Application for it utilizing Android Studio.

  • The course incorporates models from fundamental to progress
  • An extremely basic model
  • Model utilizing spared model
  • Model utilizing solid capacity
  • Anticipating eco-friendliness of cars (Regression Example)
  • Perceiving transcribed digits (Classification model)
  • Felines and Dogs order
  • Rock Paper and Scissors Problem
  • Blossoms Recognition Example
  • Stones Recognition Example
  • Natural products Recognition Example
  • Anticipating Fitness of an individual Practice Activity
  • Human and Horse Practice Activity

For every one of these models, we will initially prepare Machine Learning model at that point manufacture Android Application

We will begin by finding out about the essentials of the Python programming language. At that point we will find out about some popular Machine Learning libraries like Numpy, Matplotlib, and Pandas. From that point onward, we will find out about Machine learning and its sorts. At that point we take a gander at Supervised learning in detail. We will attempt to get arrangement and relapse through models. After we will begin Deep learning. We start by looking and the fundamental structure of neural organizations. At that point we will comprehend the working of neural organizations through a model.

At that point we will find out about the Tensorflow 2.0 library and how we can utilize it to prepare Machine Learning models. From that point onward, we will take a gander at Tensorflow light how we can change over our Machine Learning models to tflite design which will be utilized inside Android Applications. There are three different ways through which you can get a tflite record

  1. From Keras Model
  2. From Concrete Function
  3. From Saved Model

We will cover all these three strategies in this course.

We will find out about Feed Forwarding, Back Propagation, and actuation capacities through a viable model. We likewise see cost work, streamlining agent, learning rate, Overfitting, and Dropout. We will likewise find out about information preprocessing methods like One hot encoding and Data standardization.

Next, we execute a neural organization utilizing Google’s new TensorFlow library.

You should take this course If you are an Android Developer and need to gain proficiency with the essentials of machine learning(Deep Learning) and send ML models in your Android applications utilizing Tensorflow light and Android Studio.

This course gives you numerous down to earth models so you can truly perceive how you can prepare and send AI model in android. We will utilize Android Studio for creating Android Application for models we prepared.

One more area toward the finish of the course gives you how you can utilize datasets accessible in various organizations for various reasonable purposes.

Subsequent to considering making the plunge with the basics, I give a short review of how you can include your AI model in google’s current android AI venture layouts.

Recommended Prerequisites:

  • Essential Knowledge of Android App Development

TIPS (for traversing the course):

  • Compose code yourself, don’t simply stay there and take a gander at my code.

Who this course is for:

  • Amateur Android Developers need to make their Android applications savvy
  • Android Developers need to utilize Machine Learning in their Android Applications
  • Engineers inspired by the functional execution of Machine Learning and PC vision
  • Understudies keen on AI – you’ll get everything the goodies you require the most to include AI models in android utilizing Android studio
  • Experts who need to Utilize AI models in Android Application .
  • AI specialists need to send their models in Android utilizing Android studio and Tensorflow light


  • 16 sections • 79 lectures • 5h 35m total length

Introduction1 lecture • 2min

  • Introduction and course OverviewPreview02:01

Setting up the environment3 lectures • 8min

  • Setting up the environment01:58
  • Installing Tensorflow04:05
  • Jupyter Notebook Introduction02:11

Learning Python6 lectures • 15min

  • Python Introduction and data types05:42
  • Python Lists01:48
  • Python List Functions02:05
  • Python dictionary and tuples01:41
  • Python Loops and conditional statements02:03
  • Python File handling02:04

Data Science Libraries7 lectures • 16min

  • Numpy Introduction and arrays02:30
  • Numpy functions02:00
  • Numpy Operators02:39
  • Pandas Introduction02:53
  • Pandas reading files and handling missing values02:44
  • Matplotlib introduction01:30
  • Matplotlib dealing with images01:30

Machine Learning and Deep Learning6 lectures • 29min

  • Machine Learning, Classification and Regression06:35
  • Unsupervised, Reinforcement Learning01:20
  • Deep Learning02:36
  • Deep Learning Part 209:07
  • Basic Concepts Part 104:15
  • Basic Concepts Part 204:52

Tensorflow7 lectures • 19min

  • Tensorflow Introduction02:55
  • Tensorflow Constants and shaping01:41
  • Tensorflow rank and numpy02:17
  • Tensorflow Matrix multiplication and Ragged Tensors03:07
  • Tensorflow Operations02:09
  • Generating Random Values04:12
  • Saving Variables using Checkpoints02:14

Training First model and creating Android Application3 lectures • 24min

  • Creating and training first ML modelPreview08:21
  • Creating Android Application for the model Java08:55
  • Creating Android Application for the model Kotlin06:38

Concrete function and Saved model examples2 lectures • 6min

  • Concrete Function Example04:04
  • Saved Model Example02:03

Predicting Fuel Efficiency of automobiles7 lectures • 36min

  • Loading data and preprocessing04:20
  • One Hot Encoding05:06
  • Normalizing data and training model10:07
  • Fuel Efficiency Application Part 103:30
  • Java: Fuel Efficiency Application Part 204:12
  • Kotlin: Fuel Efficiency Application07:16
  • Testing Application01:41

Recognizing Handwritten digits9 lectures • 35min

  • Loading the dataset02:23
  • Matplotlib and normalizing data01:46
  • Training model03:11
  • Evaluating model and creating tflite file02:01
  • Digit Recognizer Application 108:24
  • Digit Recognizer Application Part 201:52
  • Digit Recognizer Application Part 304:49
  • Testing ApplicationPreview01:57
  • Kotlin: Digit Recognizer Android Application09:01

Recognition Section7 lectures • 29min

  • Transfer Learning02:05
  • Google Colab03:07
  • Flower Recognition loading data set04:20
  • Flower Recognition Training and evaluating model04:14
  • Flower Recognition Detailed Process07:48
  • Flower Recognition model03:44
  • Evaluating tflite model03:15

Cats and Dogs Classification3 lectures • 46min

  • Train cats and dogs model16:56
  • Java: Build Cats and dogs classification Application17:01
  • Kotlin: Build Cats and dogs classification Application12:27

Rock Paper and Scissors Problem3 lectures • 24min

  • Training the model09:18
  • Java: Rock Paper and Scissor Android Application07:32
  • Kotlin: Rock Paper and Scissor Android Application07:33

Practice Activity 1 Predict Fitness of a Person8 lectures • 18min

  • Introduction00:57
  • Practice Activity 1 Part 103:12
  • Practice Activity 1 Part 203:59
  • Practice Activity 1 Part 301:44
  • Practice Activity 1 Part 401:28
  • Practice Activity 1 Solution02:24
  • Practice Activity 1 Application 103:03
  • Practice Activity 1 Application 201:30

Practice Activity 2 Human and Horses4 lectures • 15min

  • Assignment00:41
  • Training Human and Horses model08:26
  • Java: Build Human and Horses classification Application02:42
  • Kotlin: Build Human and Horses classification Application03:12

Bonus3 lectures • 13min

  • Working with images Part 101:40
  • Working with images Part 207:37
  • Working with CSV03:13


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