|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/detecting-heart-disease-diabetes-with-machine-learning/
课程评论：没有评论
课程名称：使用机器学习检测心脏病和糖尿病 概述：欢迎参加“使用机器学习检测心脏病和糖尿病”课程。本课程是一个全面的项目驱动课程，您将逐步学习如何使用随机森林、XGBoost、逻辑回归和支持向量机来构建心脏病和糖尿病检测模型。这个课程完美结合了机器学习与医疗分析，为您提升数据科学和编程技能提供了理想机会。 在第一部分，您将了解机器学习在医疗领域的应用，包括其用例、使用的模型、患者数据隐私、技术挑战和局限性。接下来的内容将深入探讨心脏病和糖尿病检测模型的工作原理，涵盖数据收集、数据预处理、训练和测试集的划分、模型选择、模型训练及疾病检测等环节。 课程还将介绍心脏病和糖尿病的主要原因，例如高血压、高胆固醇、肥胖、过度糖分摄入和遗传因素。了解完疾病检测模型所需的知识后，您将进入项目部分。首先，课程将指导您一步步设置Google Colab IDE，并学习如何从Kaggle获取和下载临床数据集。 在项目的第一部分，您将多角度探索临床数据集，并将数据可视化，以确保理解数据模式。在第二部分，您将学习如何使用随机森林、XGBoost、逻辑回归和支持向量机逐步构建心脏病和糖尿病检测系统。同时，在第三部分，您将学会使用k折交叉验证、准确率和召回率等多种方法评估模型的准确性和性能。 最终，课程将进行疾病检测模型的测试，以确保模型功能全面且检测结果准确。在开始学习之前，我们需要思考，为什么要构建心脏病和糖尿病检测模型？答案是，机器学习为提高医疗标准提供了绝佳机会，通过早期疾病检测，帮助实现及时干预、个性化治疗计划以及主动健康管理。这不仅改善了患者的治疗效果，还能优化医疗服务体系，减少医疗提供者的压力和长期医疗费用。通过构建这些项目，您将获得宝贵的技能和知识，为您在医疗领域带来积极影响，并开启无尽的机会之门。 您可以期待从本课程中学到的内容包括： - 了解机器学习在医疗中的应用及患者数据隐私； - 掌握心脏病和糖尿病检测模型的工作原理，包括数据收集、预处理、特征提取和模型训练； - 学习心脏病和糖尿病的主要成因； - 学会如何从Kaggle找到和下载临床数据集； - 掌握数据清洗、相关性分析及特征重要性分析的方法； - 建立多种心脏病和糖尿病检测模型，并评估其性能。 通过以上学习，您将能够狗生动地应用机器学习技术，提高医疗检测的准确性和效率。
Welcome to Detecting Heart Disease & Diabetes with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build heart disease and diabetes detection models using Random Forest, XGBoost, logistic regression, and support vector machines. This course is a perfect combination between machine learning and healthcare analytics, making it an ideal opportunity for you to level up your data science and programming skills. In the introduction session, you will learn about machine learning applications in the healthcare field, such as getting to know its use cases, models that will be used, patient data privacy, technical challenges and limitations. Then, in the next section, we are going to learn how heart disease and diabetes detection models work. This section will cover data collection, data preprocessing, splitting the data into training and testing sets, model selection, mode training, and disease detection. Afterward, you will also learn about the main causes of heart disease and diabetes, for example, high blood pressure, high cholesterol, obesity, excessive sugar consumption, and genetics. After you have learnt all necessary knowledge about the disease detection model, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download clinical dataset from Kaggle. Once everything is ready, we will enter the first project section where you will explore the clinical dataset from multiple angles, not only that, you will also visualize the data and make sure you understand the data pattern. In the second part, you will learn step by step on how to build heart disease and diabetes detection systems using Random Forest, XGBoost, logistic regression, and support vector machines. Meanwhile, in the third part, you will learn to evaluate the model's accuracy and performance using several methods like k-fold cross validation, precision, and recall methods. Lastly, at the end of the course, we will conduct testing on the disease detection model to make sure it has been fully functioning and the detected result is accurate.First of all, before getting into the course, we need to ask ourselves this question, why should we build heart disease and diabetes detection models? Well, here is my answer. Machine learning presents an extraordinary opportunity to elevate healthcare standards by enabling early disease detection. By developing precise models for identifying heart disease and diabetes, we can initiate timely interventions, personalise treatment plans, and proactively manage health concerns. This not only enhances patient outcomes but also streamlines healthcare delivery systems, reducing the burden on healthcare providers and curbing healthcare expenses over time. In essence, these models signify a significant leap in leveraging technology to boost healthcare accessibility, efficiency, and affordability. Last but not least, by building these projects, you will gain valuable skills and knowledge that can empower you to make a difference in the world of healthcare and potentially open lots of doors to endless opportunities.Below are things that you can expect to learn from this course:Learn about machine learning applications in healthcare and patient data privacyLearn how heart disease and diabetes detection models work. This section will cover data collection, preprocessing, train test split, feature extraction, model training, and detectionLearn about the main causes of heart disease and diabetes, such as high blood pressure, cholesterol, smoking, excessive sugar consumption, and obesityLearn how to find and download clinical dataset from KaggleLearn how to clean dataset by removing missing values and duplicatesLearn how to find correlation between blood pressure and cholesterolLearn how to analyse demographics of heart disease patientsLearn how to perform feature importance analysis using Random ForestLearn how to build heart disease detection model using Random ForestLearn how to build heart disease detection model using Logistic RegressionLearn how to find correlation between blood glucose and insulinLearn how to analyse diabetes cases that are caused by obesityLearn how to build diabetes detection model using Support Vector MachineLearn how to build diabetes detection model using XGBoostLearn how to build diabetes detection model using K-Nearest Neighbors Learn how to evaluate the accuracy and performance of the model using precision, recall, and k-fold cross validation metrics
