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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-complete-android-machine-learning-course/
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课程名称：2024年安卓机器学习应用完全指南 课程概述： 欢迎来到《安卓机器学习应用完全指南》课程。在本课程中，您将学习如何在安卓应用中使用机器学习和计算机视觉，并培训自己的图像识别模型，无需具备任何机器学习背景知识。本课程专为没有机器学习基础的学员设计。在现代应用开发中，机器学习已成为移动应用开发中的必备技能。几乎所有应用程序都会使用机器学习，因此了解如何将机器学习模型集成到安卓（Java和Kotlin）应用中至关重要。本课程将教您如何实现这一目标。 课程内容包括： 1. 安卓中的图像处理与实时相机视频流。 2. 在安卓中使用量化和浮点TensorFlow Lite模型。 3. 使用TensorFlow Lite委托提高安卓中机器学习模型的性能。 4. 在安卓中进行图像分类、对象检测和图像分割。 5. 训练图像分类器，重训练现有的机器学习模型。 课程分为四大部分： 第一部分：处理安卓中的图像和实时相机视频。 第二部分：在安卓中使用预训练的TensorFlow Lite模型。 第三部分：使用Firebase ML Kit开发应用。 第四部分：训练图像分类模型。 具体学习内容涵盖： - 如何选择和捕获图像，以及显示实时相机内容。 - 使用流行的预训练机器学习模型进行图像分类、对象检测和图像分割。 - 使用Firebase ML Kit进行图像标记、条形码扫描、姿态估计、数字墨水识别等功能。 - 实时狗品种识别和水果识别的图像分类模型训练。 工具使用： - Android Studio进行安卓应用开发。 - Google Colab用于训练图像识别模型。 - Netron分析移动机器学习模型。 课程目标： 完成本课程后，您将能够使用Firebase ML Kit和预训练的TensorFlow Lite模型进行安卓应用开发，并能训练自己的图像分类模型，构建20多个基于机器学习和计算机视觉的安卓应用，提升您的求职竞争力。 课程适合： - 初学者Android开发者，拥有少量安卓应用开发知识。 - 中级Android开发者，想要构建强大的机器学习应用。 - 有经验的Android开发者，想将机器学习和计算机视觉模型应用于其安卓应用中。 不论您是希望成为现代Android开发者、自由职业者，还是希望尝试制作智能移动应用，这门课程都将为您提供深入的理解和实用的技能。立即加入，开始您的学习之旅吧！
Welcome to Machine Learning use in Android the Complete Guide.In this course, you will learn the use of Machine learning and computer vision in Android along with training your own image recognition models for Android applications without knowing any background knowledge of machine learning. The course is designed in such a manner that you don't need any prior knowledge of machine learning to take this course.In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used. So it's important to learn how we can integrate ML models inside Android (Java & Kotlin) applications. And this course will teach you that. And the main feature of this is you don't need to know any background knowledge of ML to integrate it inside your Android applications.What we will cover in this course?Dealing with Images in AndroidDealing with frames of live camera footage in AndroidUse of quantized and floating point tensorflow lite models in AndroidUse of tensor flow lite delegates to improve the performance of ML models in AndroidImage classification with images and live camera footage in AndroidObject Detection with Images and Live Camera footageImage Segmentation to make images transparent in AndroidUse of regression models in AndroidImage Labeling Android to recognize different thingsBarcode Scanning Android to scan barcodes and QR codesPose Estimation Android to detect human body jointsSelfie Segmentation Android to separate the background from the foreground Digital Ink Recognition Android to recognize handwritten textObject Detection Android to detect and track objectsText Recognition Android to recognize text in imagesSmart Reply Android to add auto reply suggestionText Translation Android to translate between different languagesFace Detection Android to detect faces, facial landmarks, and facial expressionsTraining image classification models for AndroidRetraining existing machine learning and computer vision models with transfer learning for Android applicationsSections:The course is divided into four main parts.Image and live camera footage in Android (Java & Kotlin)Pre-Trained Tensorflow Lite models use in Android (Java & Kotlin)Firebase ML Kit use in Android (Java & Kotlin)Training Image Classification models for Android (Java & Kotlin)1: Images and live camera footage in Android (Java & Kotlin)So in the first section, you will learn to handle both images and live camera footage in Android so that later we can use them with machine learning models. So, in that section, we will learn toChoose images from the gallery in Android (Java & Kotlin)Capture images using the camera in Android (Java & Kotlin)Displaying live camera footage in Android (Java & Kotlin) applications using camera2 APIAccessing frames of live camera footage in Android (Java & Kotlin)2: Pre-Trained Tensorflow LiteSo, after learning the use of images and live camera footage in Android in this section we will learn the use of popular pre-trained machine learning and computer vision models in Android and buildImage classification Android app (Both with images and live camera footage)Object detection Android app(Both with images and live camera footage)Image segmentation Androidapplications3: Quantization and DelegatesApart from that, we will cover all the important concepts related to Tensorflow lite likeUsing floating-point and quantized model in Android (Java & Kotlin)Use the use of Tensorflow lite Delegates to improve model performance4: Regression In AndroidAfter that, we will learn to use regression models in Android (Java & Kotlin) and build a couple of applications including aFuel Efficiency Predictor for Vehicles.5: Firebase ML KitThen the next section is related to the Firebase ML Kit. In this section, we will exploreFirebase ML KitFeatures of Firebase ML KitThen we are going to explore those features and build a number of applications includingImage Labeling Android (Java & Kotlin) to recognize different thingsBarcode Scanning Android (Java & Kotlin) to scan barcodes and QR codesPose Estimation Android (Java & Kotlin) to detect human body jointsSelfie Segmentation Android (Java & Kotlin) to separate the background from the foreground Digital Ink Recognition Android (Java & Kotlin) to recognize handwritten textObject Detection Android (Java & Kotlin) to detect and track objectsText Recognition Android (Java & Kotlin) to recognize text in imagesSmart Reply Android (Java & Kotlin)to add auto reply suggestionText Translation Android (Java & Kotlin) to translate between different languagesFace Detection Android (Java & Kotlin) to detect faces, facial landmarks, and facial expressionsCamScanner Android CloneApart from all these applications, we will be developing a clone of the famous document-scanning android application CamScanner. So in that application, we will auto-crop the document images using text recognition and improve the visibility of document Images.6: Training Image Classification ModelsAfter mastering the use of ML Models in the Android (Java & Kotlin) app development in the Third section we will learn to train our own Image Classification models without knowing any background knowledge of Machine learning and computer vision.So in that section, we will learn to train ML models using two different approaches.Dog breed Recognition using Teachable MachineFirstly we will train a dog breed recognition model using a teachable machine.Build a Real-Time Dog Breed Recognition Android (Java & Kotlin) Application.Fruit Recognition using Transfer LearningUsing transfer learning we will retrain the MobileNet model to recognize different fruits.Build a Real-Time fruit recognition Android (Java & Kotlin) application using that trained modelImages and Live Camera FootageThe course will teach you to use Machine learning and computer vision models with images and live camera footage, So that, you can build both simple and Real-Time Android applications.Android VersionThe course is completely up to date and we have used the latest Android version throughout the course.LanguageThe course is developed using both Java and Kotlin programming languages. So all the material is available in both languages.Tools:These are tools we will be using throughout the courseAndroid Studio for Android App developmentGoogle collab to train Image Recognition models.Netron to analyze mobile machine learning modelsBy the end of this course, you will be able Use Firebase ML kit in Android App development using both Java and KotlinUse pre-trained Tensorflow lite models in Android App development using Java and KotlinTrain your own Image classification models and build Android applications.You'll also have a portfolio of over 20+ machine learning and computer vision-based Android R applications that you can show to any potential employer.course requirements:This is the course for you ifYou want to make smart Android (Java & Kotlin) appsYou are interested in becoming a modern-day Android (Java & Kotlin) developer, a freelancer, launching your own projects, or just want to try your hand at making real smart mobile appsYou have no prior programming experience, or some but from a different language/platformYou want a course that teaches you the use of machine learning and computer vision in Android (Java & Kotlin) app development, in an integrated curriculum that will give you a deep understanding of all the key concepts an Android (Java & Kotlin) developer needs to know to have a successful careerWho can take this course:Beginner Android ( Java or Kotlin ) developer with very little knowledge of Android app development.Intermediate Android ( Java or Kotlin ) developer wanted to build a powerful Machine Learning-based application in AndroidExperienced Android ( Java or Kotlin ) developers wanted to use Machine Learning and computer vision models inside their Android applications.Anyone who took a basic Android ( Java or Kotlin ) mobile app development course before (like Android ( Java or Kotlin ) app development course by angela yu or other such courses). Unlike any other Android app development course, The course will teach you what matters the most.So what are you waiting for? Click on the Join button and start learning.
