
Add and customize markers on a Google map in Jetpack Compose, using marker state to track position and visibility, set Singapore as the marker, and configure title and snippet.
Implement marker click events in Google Maps for Android using onClick callbacks. Return true to consume the event and suppress the info window; return false to show it.
Enforce Manhattan bounds on Google Maps by using lat long bounds for the camera target, and restrict zoom with map properties to prevent scrolling outside the area.
Learn to customize the Google Maps camera position, including target lat long, zoom, tilt, and bearing, and animate to new markers with a one-second transition.
Apply a custom retro map style in Android by loading a retro_style.json from resources and using map style options to style roads, water, labels, and landmarks on Google Maps.
Fetch the current location with a callback, update the my location variable, and request the permission to display a marker and a 200m blue circle on Google Maps.
Fetch the current and live updated location using the fused location provider, building a high accuracy location request with a 10-second interval and a location callback.
Create and remember a cluster manager that handles clustering and rendering of marker items for Google Maps, utilizing Android context and compose local context.
Build an android maps app using Google Maps SDK, Firestore markers, MVVM with Hilt, fetch user location to draw routes between markers and enable map interactions with Jetpack Compose.
Create an expandable floating action button using a composable expandable fab with an expanded state, and reveal content with animated visibility featuring fade-in and vertical expansion at the bottom end.
Annotate the application class with Hilt Android app and declare its manifest name to enable singleton dependency injection container, supporting mvvm, clean architecture, Firestore markers latitude and longitude, design patterns.
Enable the Google Maps directions API, integrate Retrofit for fast, type-safe requests, and use a JSON converter (Moshi or Jackson) to serialize and deserialize directions data.
Transform a messy directions API response into visible routes on the map by implementing a retrofit-based directions service, with base URL, endpoint, origin, destination, and API key.
Learn to model Google Maps directions responses with three Kotlin data classes: directions response, route, and polyline; encode and decode polylines for efficient, extensible route data.
Learn to control multiple markers on a map using expandable floating action buttons to add markers, toggle placement, and draw routes between markers with a view model and mutable state.
Master machine learning with Android by exploring fundamentals and TensorFlow. Train models, run on-device inference with TensorFlow Lite, and integrate machine learning into Android apps with custom or prebuilt models.
Learn how a trained model uses features to predict the target, and how training teaches the model by showing many examples.
Explore unsupervised learning, analyzing data without labels to discover hidden patterns, grouping with clustering, dimensionality reduction like PCA, and association rule mining.
Discover Google Colab features, GPU and TPU access, notebook uploads to Drive, and preinstalled machine learning libraries like TensorFlow, PyTorch, and OpenCV. Be aware of free-tier limits and paid GPUs.
Explore arithmetic operators in Python, including addition, subtraction, multiplication, division, floor division, modulus, and exponentiation. Apply practical examples with a and b to see how these operators compute results.
Learn about comparison operators and boolean logic in Android, including equals, not equals, greater than or equal, less than or equal, and, or, and not to combine conditions.
Learn to create and manipulate lists by adding, removing, and accessing items, including negative indexing. Print lists, determine length with len, modify elements, append, remove, and loop through items.
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:
Prepare and structure datasets for mobile ML
Train custom models for Android use cases
Convert and optimize models into TensorFlow Lite (TFLite)
Deploy and run ML models efficiently on Android devices
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:
Differences between detection models and when to use each
How to train custom object detection models from scratch
How to export and integrate these models into Android apps
How to perform real-time object detection using the device camera
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.
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 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
Significantly upgraded your Android and AI skill set
This is an advanced-level course and assumes prior knowledge of Kotlin, Android Studio, and Android fundamentals.