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Course Information
Registration period*
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 11 Minute(s)
Language
English
Taught by
Mobile ML Academy by Hamza Asif
Rating
*Note: The schedule above is for reference only. Please refer to the course provider's website for the schedule.
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
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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
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BBrian Aboytes Morales
Titulo del curso engañoso, dice 2025 y todo es del 2022/2023 con librerías obsoletas que ya nofuncionan.
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NNguyen Minh Hung
thanks
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JJonathan Cruz
the best
