Size: 15.67 GiB (16827234582 Bytes) | Uploaded by: Metodone | Seeders: 5 | Leechers: 5
Release year: 12/2025
Udemy
Author: Abbass Masri - MasterCoding.io
Duration: 22h 15m
Type of distributed material: Video tutorial
Language: English
Description: This advanced course is Part 3 of an Android App Development Series, moving into advanced Android engineering and on-device machine learning for developers who want to go beyond CRUD-based apps and build intelligent, production-level applications combining mapping systems, real-time data, and ML models. You begin by mastering advanced Google Maps integration, building 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. Then you dive deep into Machine Learning on Android with an end-to-end workflow: preparing and structuring datasets for mobile ML, training custom models for Android use cases, converting and optimizing models into TensorFlow Lite (TFLite), and deploying and running ML models efficiently on Android devices.
A major focus is computer vision and object detection using industry-standard architectures like SSD MobileNet and YOLO, where you learn the differences between detection models and when to use each, how to train custom object detection models from scratch, how to export and integrate them into Android apps, and how to perform real-time object detection using the device camera. You also learn optimization techniques critical for mobile performance, including model size reduction, inference speed optimization, and resource management, ensuring apps run smoothly on real devices. The course is project-driven and implementation-focused, applying every major concept directly to Android so you clearly understand how machine learning, computer vision, and Android development work together in real products. By the end, you will have built advanced map-based Android applications, implemented AI-powered features using on-device ML, created and deployed custom TFLite object detection models, developed real-time ML-powered Android apps ready for production, and significantly upgraded your Android and AI skill set. It is an advanced-level course assuming prior knowledge of Kotlin, Android Studio, and Android fundamentals.
Video format: MP4
Video: AVC, 1920x1080, 16:9, 30fps, 696kbps
Audio: AAC, 48.0Hz, 192kbps, stereo