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所在平台: Udemy |
课程主页: https://www.udemy.com/course/diabetes-prediction-using-machine-learning-data-science-project/
课程评论：没有评论
课程名称：数据科学：使用Python进行糖尿病预测项目 [2023] 课程概述：欢迎参加“使用Python进行糖尿病预测项目”课程。课程将教您如何使用Python构建和评估机器学习模型。 课程介绍：您将学习如何使用支持向量机（SVM）算法进行糖尿病预测。课程中将使用真实的糖尿病数据，进行训练和测试分割，并构建预测模型以识别新病例。 数据收集与准备：您将学习如何下载和准备真实的糖尿病数据，包括计算平均值和统计糖尿病患者与非糖尿病患者的人数。 训练和测试分割：您将学习如何进行训练和测试分割，这是评估预测模型性能的关键步骤。 支持向量机（SVM）算法：本部分将覆盖SVM的基础知识，包括其数学基础及其在糖尿病预测中的应用。 构建预测模型：您将使用SVM算法构建预测模型，用于识别新出现的糖尿病病例。同时，您将学习如何评估模型的准确性，以及理解导致糖尿病风险的因素。 模型评估：您将学习如何对模型性能进行评估，包括准确性和精确度评分。 结论：到课程结束时，您将全面了解如何使用SVM进行糖尿病预测，具备构建预测系统的能力，以识别新病例。该课程涵盖数据收集与准备、机器学习算法、模型构建与评估等基本技能和概念，适合希望在数据科学和机器学习领域取得成功的学生。通过实用的实践方法，本课程是希望提高数据科学和机器学习技能并将其应用于真实问题的学习者的绝佳资源。 感谢您对本课程的关注…期待在课程中见到您！
Welcome to the course on "Diabetes Prediction Project with Python" - In this course You will learn to build and evaluate a machine learning model using python.Introduction:In this course, you will learn how to use the Support Vector Machine (SVM) algorithm for diabetes prediction. You will work with real-world diabetes data, perform train and test split, and build a predictive model to identify new cases of diabetes.Data Collection and Preparation:You will learn how to download and prepare real-world diabetes data, including calculating mean values and counting the number of people affected by diabetes and those who are not.Train and Test Split:You will learn how to perform train and test split, which is a critical step in evaluating the performance of predictive models.Support Vector Machine (SVM) Algorithm:This section will cover the basics of SVM, including its mathematical foundations and how it can be used for diabetes prediction.Building the Predictive Model:You will use the SVM algorithm to build a predictive model that can be used to identify new cases of diabetes. You will also learn how to evaluate the accuracy of the models and understand the factors that contribute to diabetes risk.Evaluating the Model:You will learn how to evaluate the performance of their models, including accuracy, precision score. Conclusion:By the end of the course, you will have a complete understanding of how to use SVM for diabetes prediction and the skills necessary to build a predictive system that can be used to identify new cases of diabetes. This course covers all the necessary skills and concepts for students to succeed in the field of data science and machine learning, including data collection and preparation, machine learning algorithms, model building and evaluation, and more. With its practical, hands-on approach, this course is an excellent resource for anyone looking to advance their skills in data science and machine learning and apply them to real-world problems.Thank you for your interest in this course...I will see you in the course...
