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The Complete Android 16 Developer Course [Part 3] - Advanced
Published 12/2025
Created by Abbass Masri - Doc. Ali Alaeddine
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 289 Lectures ( 22h 15m ) | Size: 11.8 GB [/center]
Advanced Android Development with Google Maps, Machine Learning, YOLO & TensorFlow. Become the Master
What you'll learn
Intermediate Android developers who already understand Android fundamentals and want to move into advanced, real-world app development.
Android developers interested in machine learning, computer vision, and AI-powered mobile applications.
Developers who want to integrate Google Maps and build real-world apps such as Uber-like location-based applications.
Machine learning beginners who want to apply ML concepts practically inside Android apps (no heavy math required).
Developers who want to create, train, and deploy custom ML models using TensorFlow Lite (TFLite).
Android engineers aiming to build object detection apps, including custom YOLO and SSD MobileNet models.
Students or professionals preparing for advanced Android, AI, or computer vision projects.
Developers looking to upgrade their portfolio with advanced Android + ML projects.
Requirements
Basic Android Studio skills
Description
Welcome to Part 3 of the Android App Development Series, where we move into advanced Android engineering and on-device machine learning.This course is built for developers who want to go beyond traditional CRUD-based apps and start developing intelligent, production-level Android applications that combine mapping systems, real-time data, and machine learning models.You will begin by mastering advanced Google Maps integration, learning how to build Uber-style applications that handle live location tracking, camera movement, markers, polyline routing, distance calculations, and map-based UI optimization for real-world use cases.Next, you will dive deep into Machine Learning on Android, focusing on end-to-end workflows rather than isolated concepts. You will learn how to
repare and structure datasets for mobile MLTrain custom models for Android use casesConvert and optimize models into TensorFlow Lite (TFLite)Deploy and run ML models efficiently on Android devicesA major focus of this course is computer vision and object detection. You will work with industry-standard architectures such as SSD MobileNet and YOLO, learning
ifferences between detection models and when to use eachHow to train custom object detection models from scratchHow to export and integrate these models into Android appsHow to perform real-time object detection using the device cameraYou will also learn optimization techniques critical for mobile performance, including model size reduction, inference speed optimization, and resource management, ensuring your apps run smoothly on real devices.This course is project-driven and implementation-focused. Every major concept is applied directly to Android, giving you a clear understanding of how machine learning, computer vision, and Android development work together in real products.By the end of this course, you will have:Built advanced, map-based Android applicationsImplemented AI-powered features using on-device MLCreated and deployed custom TFLite object detection modelsDeveloped real-time ML-powered Android apps ready for productionSignificantly upgraded your Android and AI skill setThis is an advanced-level course and assumes prior knowledge of Kotlin, Android Studio, and Android fundamentals.
Who this course is for
This course is designed for Android developers who already have basic to intermediate experience and want to take their skills to an advanced, professional level.
Developers who want to build real-world Android applications using Google Maps, location services, and Uber-style app concepts.
Android developers interested in integrating machine learning and computer vision into their apps.
Students and professionals who want to strengthen their portfolio with advanced Android + ML projects.
Programmers who want hands-on experience with AI-powered Android applications, not just theory.
Developers aiming to build object detection apps using YOLO and SSD MobileNet models.
The Complete Android 16 Developer Course [Part 3] - Advanced
Published 12/2025
Created by Abbass Masri - Doc. Ali Alaeddine
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 289 Lectures ( 22h 15m ) | Size: 11.8 GB [/center]
Advanced Android Development with Google Maps, Machine Learning, YOLO & TensorFlow. Become the Master
What you'll learn
Intermediate Android developers who already understand Android fundamentals and want to move into advanced, real-world app development.
Android developers interested in machine learning, computer vision, and AI-powered mobile applications.
Developers who want to integrate Google Maps and build real-world apps such as Uber-like location-based applications.
Machine learning beginners who want to apply ML concepts practically inside Android apps (no heavy math required).
Developers who want to create, train, and deploy custom ML models using TensorFlow Lite (TFLite).
Android engineers aiming to build object detection apps, including custom YOLO and SSD MobileNet models.
Students or professionals preparing for advanced Android, AI, or computer vision projects.
Developers looking to upgrade their portfolio with advanced Android + ML projects.
Requirements
Basic Android Studio skills
Description
Welcome to Part 3 of the Android App Development Series, where we move into advanced Android engineering and on-device machine learning.This course is built for developers who want to go beyond traditional CRUD-based apps and start developing intelligent, production-level Android applications that combine mapping systems, real-time data, and machine learning models.You will begin by mastering advanced Google Maps integration, learning how to build Uber-style applications that handle live location tracking, camera movement, markers, polyline routing, distance calculations, and map-based UI optimization for real-world use cases.Next, you will dive deep into Machine Learning on Android, focusing on end-to-end workflows rather than isolated concepts. You will learn how to
repare and structure datasets for mobile MLTrain custom models for Android use casesConvert and optimize models into TensorFlow Lite (TFLite)Deploy and run ML models efficiently on Android devicesA major focus of this course is computer vision and object detection. You will work with industry-standard architectures such as SSD MobileNet and YOLO, learningifferences between detection models and when to use eachHow to train custom object detection models from scratchHow to export and integrate these models into Android appsHow to perform real-time object detection using the device cameraYou will also learn optimization techniques critical for mobile performance, including model size reduction, inference speed optimization, and resource management, ensuring your apps run smoothly on real devices.This course is project-driven and implementation-focused. Every major concept is applied directly to Android, giving you a clear understanding of how machine learning, computer vision, and Android development work together in real products.By the end of this course, you will have:Built advanced, map-based Android applicationsImplemented AI-powered features using on-device MLCreated and deployed custom TFLite object detection modelsDeveloped real-time ML-powered Android apps ready for productionSignificantly upgraded your Android and AI skill setThis is an advanced-level course and assumes prior knowledge of Kotlin, Android Studio, and Android fundamentals.Who this course is for
This course is designed for Android developers who already have basic to intermediate experience and want to take their skills to an advanced, professional level.
Developers who want to build real-world Android applications using Google Maps, location services, and Uber-style app concepts.
Android developers interested in integrating machine learning and computer vision into their apps.
Students and professionals who want to strengthen their portfolio with advanced Android + ML projects.
Programmers who want hands-on experience with AI-powered Android applications, not just theory.
Developers aiming to build object detection apps using YOLO and SSD MobileNet models.
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