Course Information
Course Overview
Train custom object detection models and deploy them in Android apps using TensorFlow Lite, Kotlin & Java in Realtime
Mobile AI is shifting from cloud to on-device. With TensorFlow Lite (TFLite) you can run real-time object detection directly on Android phones—no server, zero latency. This course gives you an end-to-end workflow to train, convert, and deploy custom models using Kotlin and Java.
What You’ll Master
Data Collection & Annotation
Capture images and label them with LabelImg, CVAT, or Roboflow to create high-quality datasets.Model Training in TensorFlow / YOLO / EfficientDet / SSD-MobileNet
Hands-on Colab notebooks show you how to train from scratch or fine-tune pre-trained weights.TFLite Conversion & Optimization
Quantize, prune, and add metadata for maximum FPS and minimum battery drain.Android Integration (CameraX + ML Model Binding)
Build apps in Kotlin or Java that detect objects in both images and live camera streams.Using Pre-Trained Models
Plug in ready-made YOLOv8-Nano, EfficientDet-Lite, or SSD-MobileNet with just a few lines of code.
Included Resources
Production-ready Android templates (Kotlin & Java) worth $1,000+
Re-usable model-conversion scripts and Colab notebooks
Pre-annotated sample dataset to get you started fast
Cheatsheets for common TFLite errors and performance tuning
Real-World Use-Cases You’ll Build
Smart CCTV with intrusion alerts
Industrial defect detection on assembly lines
Crowd counting & retail analytics dashboards
Prototype modules for self-driving or AR apps
Who Should Enroll?
Android developers eager to add on-device AI (beginner to pro)
ML engineers targeting mobile deployment and edge-AI optimization
Makers, startup founders, or hobbyists who want to build vision-powered apps without a backend
What You Need
Basic Android Studio familiarity (layouts, activities, Gradle)
Light Python knowledge (all heavy lifting handled in the provided notebooks)
A computer with 8 GB RAM—heavy training runs on free Google Colab GPUs
Course Format
1080p HD video lectures (updated for Android Studio 2025 & TensorFlow Lite 3.x)
Mini-projects after each section to cement skills
Lifetime access, Q&A support, and Udemy’s 30-day money-back guarantee
Ready to build fast, reliable object-detection apps that run entirely on Android devices?
Click Buy Now and start training & deploying your own TFLite models today!
Course Content
- 10 section(s)
- 103 lecture(s)
- Section 1 Introduction
- Section 2 Dataset Collection and Annotation for Object Detection Model Training
- Section 3 Training Custom Object Detection models for Android Apps
- Section 4 Java: Choose or Capture Images in Android
- Section 5 Kotlin: Choose or Capture Images in Android
- Section 6 Java: Object Detection with Images for Android App Development
- Section 7 Kotlin: Object Detection with Images for Android App Development
- Section 8 Java: Object Detection with Live Camera Footage in Android
- Section 9 Kotlin: Real Time Object Detection in Android
- Section 10 Java: EfficientDet Models for Object Detection in Android App Development
What You’ll Learn
- Train Object Detection Models from Scratch and Convert them to Tensorflow Lite Format
- Use Trained Object Detection Models in Android (Java / Kotlin) with both Images and Videos
- Learn to Collect and Annotate Datasets for Training Object Detection Models
- Learn basics of Object Detection ,its applications and Machine Learning for Training Machine Learning Models for Mobile Devices
- Learn to use SSD EfficientDet Models in Android (Java / Kotlin) for Object Detection
- Learn to use SSD MobileNet Models in Android (Java / Kotlin) for Object Detection
- Learn use of YOLO Models in Android (Java / Kotlin) for Object Detection
Skills covered in this course
Reviews
-
UU 2
Some parts of the training are outdated and don't work - I tried to contact the trainer (and I not alone, others have the same probem) but I have zero answer - Avoid this course
-
BBrian Aboytes Morales
Titulo del curso engañoso, dice 2025 y todo es del 2022/2023 con librerías obsoletas que ya nofuncionan.
-
NNguyen Minh Hung
thanks
-
JJonathan Cruz
the best