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所在平台: Udemy |
课程主页: https://www.udemy.com/course/end-to-end-data-science-and-machine-learning-project/
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Coursera 课程总结：端到端数据科学与机器学习项目 - 葡萄酒品质预测 本课程将引导您完成一个完整的数据科学和机器学习项目，专注于葡萄酒品质的预测。您将学习如何端到端地处理数据集，并构建一个能够预测葡萄酒品质的机器学习模型。 **课程主要内容：** * **数据集介绍：** 课程将使用包含葡萄牙 Vinho Verde 葡萄酒（红、白两款）的数据集。该数据集包含 1599 个红葡萄酒样本和 4898 个白葡萄酒样本。输入变量包括葡萄酒类型以及客观测试指标（如酸度、pH 值、酒精度等）。 * **技术要求：** 课程需要您具备 Python 编程知识。所有实践操作将在 Google Colab 中进行，无需另外安装 Python 或 Anaconda。同时，需要有基础的机器学习和数据科学知识，课程中也会讲解所使用模型的理论和实践应用。 * **项目类型：** 本项目属于分类问题，是监督学习算法的一个应用。葡萄酒的“品质”将被设为目标变量，其他变量作为输入特征。通过训练模型，预测未知葡萄酒的品质。 * **学习内容：** 您将学习如何使用决策树（Decision Trees）和逻辑回归（Logistic Regression）等模型。此外，还将学习如何使用 LazyPredict 库加速模型选择，以及如何通过网格搜索（Grid Search）对模型超参数进行优化。 **目标：** 通过本课程的学习，您将能够独立完成一个从数据收集、预处理、模型选择、训练到优化的端到端数据科学和机器学习项目。
Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don't worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.You will learn to work with Decision Trees, Logistic Regression, how to use LazyPredict and how to tune the hyperparameters using Grid Search.
