The Complete Android 16 Course [Part 3] - Become a Master

<div>Welcome to Part 3 of the Android App Development Series, where we move into advanced Android engineering and on-device machine learning.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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:</div><div><ul><li><span style="font-size: 1rem;">Prepare and structure datasets for mobile ML</span></li><li><span style="font-size: 1rem;">Train custom models for Android use cases</span></li><li><span style="font-size: 1rem;">Convert and optimize models into TensorFlow Lite (TFLite)</span></li><li><span style="font-size: 1rem;">Deploy and run ML models efficiently on Android devices</span></li></ul></div><div><br></div><div>A 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:</div><div><ul><li><span style="font-size: 1rem;">Differences between detection models and when to use each</span></li><li><span style="font-size: 1rem;">How to train custom object detection models from scratch</span></li><li><span style="font-size: 1rem;">How to export and integrate these models into Android apps</span></li><li><span style="font-size: 1rem;">How to perform real-time object detection using the device camera</span></li></ul></div><div><br></div><div>You 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.</div><div><br></div><div>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.</div><div><br></div><div>By the end of this course, you will have:</div><div><ul><li><span style="font-size: 1rem;">Built advanced, map-based Android applications</span></li><li><span style="font-size: 1rem;">Implemented AI-powered features using on-device ML</span></li><li><span style="font-size: 1rem;">Created and deployed custom TFLite object detection models</span></li><li><span style="font-size: 1rem;">Developed real-time ML-powered Android apps ready for production</span></li><li><span style="font-size: 1rem;">Significantly upgraded your Android and AI skill set</span></li></ul></div><div><br></div><div>This is an advanced-level course and assumes prior knowledge of Kotlin, Android Studio, and Android fundamentals.</div>

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.