- Introduction and course Overview
Beginner
Online
₹ 649 799
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
The Android Machine Learning with TensorFlow lite in Java/Kotlin certification course was designed by Hamza Asif, an Android Developer, Flutter Developer, and Instructor, and is made available through Udemy, which is aimed at students who want to become proficient in the machine learning tools and methodologies to implement machine learning models in their android applications using the functionalities of TensorFlow Lite.
Android Machine Learning with TensorFlow lite in Java/Kotlin online classes encompass more than 5.5 hours of prerecorded lectures supported by 19 downloadable study materials which are geared toward teaching students how to use Android Studio and TensorFlow Lite to incorporate machine learning and computer vision into their applications for Android. Android Machine Learning with TensorFlow lite in Java/Kotlin online course discusses topics like application development, classification, and regression as well as explains the functioning of various tools like Keras, Java, Kotlin, Pandas, Numpy, and Matplotlib.
The highlights
- Certificate of completion
- Self-paced course
- 5.5 hours of pre-recorded video content
- 19 downloadable resources
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Android Machine Learning with TensorFlow lite in Java/Kotlin online certification, students will gain a better understanding of the strategies used to develop applications in Android studio for the android platform using principles of deep learning and machine learning with Tensorflow Lite in Java and Kotlin. In this machine learning course, students will explore the fundamentals of TensorFlow and TensorFlow 2.0 as well as will acquire knowledge of the functionalities of the data science libraries like Pandas, NumPy, and Matplotlib. In this machine learning certification, students will learn about concepts involved with regression, and classification as well as will acquire the skills to work with neural networks and machine learning models.
The syllabus
Introduction
Setting up the environment
- Setting up the environment
- Installing Tensorflow
- Jupyter Notebook Introduction
Learning Python
- Android ML: Python Introduction and data types
- Android ML: Python Lists
- Android ML: Python List Functions
- Android ML: Python dictionary and tuples
- Android ML: Python Loops and conditional statements
- Android ML: Python File handling
Data Science Libraries
- Android ML: Numpy Introduction and arrays
- Android ML: Numpy functions
- Android ML: Numpy Operators
- Android ML: Pandas Introduction
- Android ML: Pandas reading files and handling missing values
- Android ML: Matplotlib introduction
- Android ML: Matplotlib dealing with images
Machine Learning and Deep Learning
- Android ML: Machine Learning, Classification and Regression
- Android ML: Unsupervised, Reinforcement Learning
- Android ML: Deep Learning
- Android ML: Deep Learning Part 2
- Basic Concepts Part 1
- Basic Concepts Part 2
Tensorflow
- Android ML: Tensorflow Introduction
- Android ML: Tensorflow Constants and shaping
- Android ML: Tensorflow rank and numpy
- Android ML: Tensorflow Matrix multiplication and Ragged Tensors
- Android ML: Tensorflow Operations
- Android ML: Generating Random Values
- Android ML: Saving Variables using Checkpoints
Training First model and creating Android Application
- Creating and training first ML model for android
- Java: Creating Android Application for the Machine learning model
- Java: Testing Machine learning based Android Applications
- Kotlin: Creating Android Application for the model
Concrete function and Saved model examples
- Android Tensorflow lite Concrete Function Example
- Android Tensorflow lite Saved Model Example
Predicting Fuel Efficiency of automobiles
- Android ML: Loading data and preprocessing
- Android ML: One Hot Encoding
- Android ML: Normalizing data and training model
- Android ML Java: Fuel Efficiency Android Application Part 1
- Android ML Java: Fuel Efficiency Application Part 2
- Android ML Kotlin: Fuel Efficiency Application
- Android ML: Testing Fuel Efficiency prediction Application
Handwritten digits recognition application
- Android ML: Loading the dataset
- Android ML: Matplotlib and normalizing data
- Android ML: Training digit recognition model
- Android ML: Evaluating model and creating tflite file
- Android ML: Digit Recognizer Application 1
- Android ML: Digit Recognizer Application Part 2
- Android ML: Digit Recognizer Application Part 3
- Android ML: Testing digit recognition Application
- Kotlin: Digit Recognizer Android Application
Recognition Section
- Android ML: Transfer Learning
- Android ML: Google Colab
- Android ML: Flower Recognition loading data set
- Android ML: Flower Recognition Training and evaluating model
- Android ML: Flower Recognition Detailed Process
- Android ML: Flower Recognition model
- Android ML: Evaluating tflite model
Cats and Dogs Classification
- Android ML: Train cats and dogs model
- Android ML Java: Build Cats and dogs classification Application
- Android ML Kotlin: Build Cats and dogs classification Application
Rock Paper and Scissors Problem
- Android ML: Training rock paper scissors model
- Android ML Java: Rock Paper and Scissor Android Application
- Android ML Kotlin: Rock Paper and Scissor Android Application
Practice Activity 1 Predict Fitness of a Person
- Android ML: Introduction
- Android ML: Fitness Practice Activity 1 Part 1
- Android ML: Fitness Practice Activity 1 Part 2
- Android ML: Fitness Practice Activity 1 Part 3
- Android ML: Fitness Practice Activity 1 Part 4
- Android ML: Fitness Practice Activity 1 Solution
- Android ML: Fitness Practice Activity 1 Application 1
- Android ML: Fitness Practice Activity 1 Application 2
Practice Activity 2 Human and Horses
- Android ML: Human and horses Assignment
- Android ML: Training Human and Horses model
- Android ML Java: Build Human and Horses classification Application
- Android ML Kotlin: Build Human and Horses classification Application
Bonus
- Android ML: Working with images Part 1
- Android ML: Working with images Part 2
- Android ML: Working with CSV
Instructors
Mr Hamza Asif
Android Developer
Freelancer
Articles
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