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所在平台: Udemy |
课程主页: https://www.udemy.com/course/android-machine-learning-with-tensorflow-lite-using_kotlin_masterclass/
课程评论：没有评论
课程名称：Android 15与机器学习 - 在Android上训练TensorFlow Lite模型 课程概述：如果您希望训练不同的机器学习模型并构建智能Android应用程序，欢迎参加本课程。在本课程中，您将学习如何从零开始训练强大的图像分类、目标检测和线性回归模型。课程包含使用自定义训练的机器学习模型和现有TensorFlow Lite模型在Android应用中运用的知识。 ### 主要内容 1. **线性回归**：学习如何使用回归技术预测例如房价、车辆燃油效率、药物剂量推荐等。您将训练自定义线性回归模型，转为TensorFlow Lite格式，并在Android应用中使用。 2. **图像分类**：掌握识别图像中不同实体的技术，包括动物、植物、疾病、食品等。图像分类可用于电子商务应用中的产品分类、移动应用中的视觉搜索以及医疗应用中的疾病诊断。 3. **目标检测**：学习如何识别和定位图像或视频中的各种对象，如汽车、行人等。这项技术在自动驾驶、监控、零售、医疗、农业及智能城市等领域具有广泛应用。 ### 学习目标 - 理解机器学习及其深度学习基础。 - 使用Python和数据科学库（如NumPy、Pandas、Matplotlib）准备和分析数据集。 - 利用TensorFlow进行模型训练，并转化为适用于移动设备的TFLite格式。 - 在Android中实现基本的线性回归模型与图像分类、目标检测模型。 ### 适合对象 - 渴望将预测建模技能加入自己技能组合的初学者Android开发者。 - 想要构建强大机器学习基础应用的中级Android开发者。 - 经验丰富的Android开发者，期望在应用中运用机器学习模型。 ### 总结 加入我们，探索Android与机器学习的结合，结束时您将能够开发出不仅外观出色还具备智能决策能力的Android应用。立即注册，开启这一令人兴奋的学习旅程吧！
Do you want to train different Machine Learning models and build smart Android applications then Welcome to this course.In this course, you will learn to train powerfulImage ClassificationObject DetectionLinear Regressionmodel in python from scratch. After that you will learn toUse your custom trained Machine Learning Models in AndroidUse existing tensorflow lite models in Android AppsRegressionRegression is one of the fundamental techniques in Machine Learning which can be used for countless applications. Like you can train Machine Learning models using regression to predict the price of the houseto predict the Fuel Efficiency of vehiclesto recommend drug doses for medical conditionsto recommend fertilizer in agriculture to suggest exercises for improvement in player performanceand so on. So Inside this course, you will learn to train your custom linear regression models in Tensorflow Lite format and build smart Android Applications.Image Classification & ApplicationsImage classification is the process of recognizing different entities or things in an image or video. You can recognize animals, plants, diseases, food, activities, colors, things, fictional characters, drinks, etc with image recognition.In e-commerce applications image classification can be used to categorize products based on their visual features, So it is used to organize products into categories for easy browsing.Image classification can be used to power visual search in mobile apps, so users can take a picture of an object and then find similar items for sale.Image classification can be used in medical apps to diagnose disease based on medical images, such as X-rays or CT scans.We can use image classification to build countless recognition applications for performing number of tasks, like we can train a model and build applications to recognizeDifferent Breeds of dogsDifferent Types of plantsDifferent Species of AnimalsDifferent kind of precious stonesImage Classification & ApplicationsObject detection is a powerful computer vision technique that can accurately identify and pinpoint the location of various objects within images or videos. By recognizing objects like cars, people, and animals, this technology empowers applications such as security surveillance, autonomous vehicles, and smartphone apps that can identify objects through the camera lens.Key Applications:Autonomous Vehicles: Cars equipped with object detection can safely navigate roads, avoid collisions, and enhance driver assistance systems.Surveillance Systems: Security cameras can identify individuals, track suspicious activity, and detect intrusions.Retail: Stores can monitor customer behavior, manage inventory, and prevent theft.Healthcare: Medical imaging systems can detect anomalies like tumors and fractures.Agriculture: Farmers can monitor crops, livestock, and detect pests or diseases.Manufacturing: Quality control and automation can be improved through object inspection and robotic guidance.Sports Analytics: Tracking player movements and equipment can enhance performance analysis and fan experience.Environmental Monitoring: Wildlife conservation and habitat protection can benefit from object detection.Smart Cities: Traffic management, public space monitoring, and waste management can be optimized.I'm Muhammad Hamza Asif, and in this course, we'll embark on a journey to combine the power of predictive modeling with the flexibility of Android app development. Whether you're a seasoned Android developer or new to the scene, this course has something valuable to offer youCourse Overview: We'll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Android.The Android-ML Fusion: After grasping the core concepts, we'll bridge the gap between Android and Machine Learning. To do this, we'll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model trainingUnlocking Data's Power: To prepare and analyze our datasets effectively, we'll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data's potential for accurate predictions.Tensorflow for Mobile: Next, we'll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including AndroidRegression Models TrainingTraining Your First Machine Learning Model:Harness TensorFlow and Python to create a simple linear regression modelConvert the model into TFLite format, making it compatible with AndroidLearn to integrate the tflite model into Android apps for AndroidFuel Efficiency Prediction:Apply your knowledge to a real-world problem by predicting automobile fuel efficiencySeamlessly integrate the model into a Android app for an intuitive fuel efficiency prediction experienceHouse Price Prediction in Android:Master the art of training machine learning models on substantial datasetsUtilize the trained model within your Android app to predict house prices confidentlyComputer Vision Model TrainingImage Classification in Android:Collect and process dataset for model trainingTrain image classification models on custom datasets with Teachable MachineTrain image classification models on custom datasets with Transfer LearningUse image classification models in Android with both images and live camera footageObject Detection in AndroidCollect and Annotate Dataset for Object Detection Model TrainingTrain Object Detection ModelsUse object detection models in Android with Images & VideosThe Android Advantage: By the end of this course, you'll be equipped to:Train advanced machine learning models for accurate predictionsSeamlessly integrate tflite models into your Android applicationsAnalyze and use existing regression & vision (ML) models effectively within the Android ecosystemWho Should Enroll:Aspiring Android developers eager to add predictive modeling to their skillsetBeginner Android developer with very little knowledge of mobile app development Intermediate Android developer wanted to build a powerful Machine Learning-based applicationExperienced Android developers wanted to use Machine Learning models inside their applications.Step into the World of Android and Machine Learning: Join us on this exciting journey and unlock the potential of Android and Machine Learning. By the end of the course, you'll be ready to develop Android applications that not only look great but also make informed, data-driven decisions.Enroll now and embrace the fusion of Android and Machine Learning!
