On-Device Machine Learning Train & Deploy Models in Mobile
On-Device Machine Learning : Train & Deploy Models in Mobile
Published 11/2024
Created by Mobile ML Academy by Hamza Asif
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 236 Lectures ( 19h 24m ) | Size: 14 GB
Hands-on course for training ML models and deploying in Mobile Apps with Tensorflow Lite - Build On-Device Mobile Apps
What you'll learn
Train image classification models using TensorFlow and Teachable Machine, and optimize them for mobile deployment.
Collect and annotate data, train an object detection model, and convert it to TensorFlow Lite.
Build and train a linear regression model and prepare it for mobile use with TensorFlow Lite.
Integrate trained models into mobile applications for Android(Kotlin), iOS(Swift), and Flutter(Dart) platforms.
Understand the basics of machine learning, its types, and how it's applied in real-world scenarios.
Gain foundational knowledge in deep learning and how artificial neural networks work.
Build functional apps that leverage ML models, such as prediction apps, image classifiers, and object detectors with image and video processing.
Learn key Python syntax and data science libraries, including NumPy, Pandas, and Matplotlib.
Master TensorFlow for model training and TensorFlow Lite for model optimization and deployment on mobile.
Train more advanced models like fuel efficiency and house price predictions, then convert them to TensorFlow Lite.
Requirements
No prior experience in machine learning, deep learning, or app development is required, but a willingness to learn these topics will be beneficial.
Description
Train Deep Learning Models & Deploy in Mobile Apps on DeviceUnlock the potential of Machine Learning (ML) and mobile app development in one comprehensive course! Designed for beginners and experienced developers alike, this course will take you from foundational ML concepts to building and deploying intelligent mobile applications on Android, Flutter, and iOS.What You'll Learn:Fundamentals of Machine Learning & Deep Learning: Start with the basics of machine learning, understanding its types, applications, and impact on modern technology. Then, dive deeper into deep learning and artificial neural networks to understand how they mimic the human brain to make predictions.Python Programming & Essential Data Science Libraries: Familiarize yourself with Python, the go-to language for data science, ML, and deep learning. You'll gain hands-on experience with powerful libraries like NumPy for data manipulation, Pandas for data analysis, and Matplotlib for data visualization, forming the backbone of your data science toolkit.TensorFlow & TensorFlow Lite for Model Training: Learn the fundamentals of TensorFlow, the industry-standard framework for training robust machine learning models. You'll also discover TensorFlow Lite, which enables you to optimize and deploy models to mobile devices, making ML integration into apps seamless and efficient.Model Training & Conversion to TensorFlow Lite:Linear Regression & Predictive Models: Begin with a simple linear regression model, mastering the basics of model training and evaluation. Then, move on to practical projects like predicting fuel efficiency and house prices, and convert these models to TensorFlow Lite for easy mobile deployment.Image Classification: Learn image classification using two approaches. First, train a neural network in Python, and then explore Google's free, drag-and-drop tool, Teachable Machine, for a no-code approach. Once trained, you'll convert these models to TensorFlow Lite to make them mobile-ready.Object Detection with Transfer Learning: Take your skills further by learning object detection. Collect and annotate data, and train an object detection model using transfer learning for faster, more accurate results. Once trained, convert the model to TensorFlow Lite for mobile deployment.Mobile App Development with ML Models:Once your models are ready, dive into the exciting world of mobile app development! You'll learn how to integrate these machine learning models into mobile applications on three popular platforms:Flutter (Dart): Begin by using linear regression models to build apps for predicting simple values, fuel efficiency, and house prices. Then, work with image classification models in Flutter to classify images and videos. Lastly, integrate object detection models to recognize objects within images and videos on your Flutter app.Native Android (Kotlin): Repeat the process on Android, using Kotlin to build apps that integrate your regression, classification, and object detection models. You'll gain experience with Android's ML and camera APIs, ensuring your apps can process both images and live video footage.iOS (Swift): Finally, you'll integrate linear regression models into iOS apps using Swift, making it simple for users to make predictions directly on their iOS devices.Why Take This Course?By the end of this course, you'll have mastered the complete ML workflow — from training models in Python to deploying them in mobile applications. Whether you're a developer wanting to integrate AI into your apps or a data scientist aiming to expand your skills into mobile, this course is tailored for you.What You'll Build:Linear Regression Apps for price and efficiency predictionsImage Classification Apps with both images and videosObject Detection Apps with live detection capabilitiesTake the leap and transform your skills in machine learning and mobile development. Enroll today, and let's start building intelligent mobile apps together!
Who this course is for
Anyone who wants to create intelligent mobile applications that leverage ML models for real-world use cases.
Anyone new to machine learning who wants a hands-on, beginner-friendly course with practical applications.
Individuals with some data science background looking to expand their skills to mobile ML model deployment.
Android, iOS, and Flutter developers who want to add machine learning capabilities to their apps.
Developers interested in learning how to integrate machine learning models into mobile applications.
Professionals looking to offer machine learning app development services for clients
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