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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/train-machine-learning-models
课程评论：没有评论
课程名称：训练机器学习模型 概述：本课程旨在为希望了解机器学习基本概念的商业专业人士提供指导。学员将学习如何通过实验设计测试模型假设，并使用能够解决分类、回归、预测和聚类问题的算法来训练、调整和评估模型。成功完成本课程的学员需要具有计算技术的背景，并具备一定的计算机编程能力。 课程大纲： 第一部分：准备训练机器学习模型 描述：在CDSP专业化的前几门课程中，您的数据经过了大量准备。现在是开始开发机器学习模型的时机。这些模型将在实现您的商业目标中发挥重要作用，因为它们能够智能地估计世界上的许多事物。在开始构建这些模型之前，您需要牢牢把握机器学习的基本构成以及如何运用机器学习来测试假设。 第二部分：开发分类模型 描述：您将构建的第一类机器学习任务是分类。分类在许多不同领域有着广泛的应用，因此是一个很好的起点。在本模块中，您将训练分类模型，调整这些模型，并在迭代改进的过程中评估它们。 第三部分：开发回归模型 描述：您将要进行的下一个主要机器学习任务是回归。分类是将事物归入类别，而回归则是关于数字估计。在本模块中，您将训练、调整并评估执行回归的模型。 第四部分：开发聚类模型 描述：您已经使用分类和回归构建了监督学习模型。现在是时候处理无监督学习了，即标记数据不容易获得。在本模块中，您将实现无监督学习，形式为聚类模型，可以将具有共同特征的观察结果分组。和之前一样，您将通过训练、调整和评估的过程来开发这些模型。 第五部分：应用所学知识 描述：您已经为分类、回归和聚类开发了模型。在本模块中，您将应用所学内容，进行实际场景中的机器学习任务。您将使用Jupyter notebook执行机器学习任务，并可选择三种不同算法的笔记本。
Part: 1
Title:Prepare to Train a Machine Learning Model
Description:In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.
Part: 2
Title:Develop Classification Models
Description:The first type of machine learning task you'll build models for is classification. Classification has many applications across many different fields, so it's a good starting point. In this module, you'll train classification models, tune those models, and then evaluate them as part of a process of iterative improvement.
Part: 3
Title:Develop Regression Models
Description:The next major machine learning task you'll undertake is regression. Whereas classification is about placing things in categories, regression is about estimating numbers. As with the previous module, in this module you'll train, tune, and then evaluate models that perform regression.
Part: 4
Title:Develop Clustering Models
Description:You've built supervised learning models using both classification and regression. But now it's time to work with unsupervised learning, where labeled data is not readily available. In this module, you'll implement unsupervised learning in the form of clustering models, which can group observations that share common traits. Just like before, you'll develop these models as a process of training, tuning, and evaluation.
Part: 5
Title:Apply What You've Learned
Description:You have developed models for classification, regression and clustering, in this module you will apply what you have learned working within a practical scenario. Using a Jupyter notebook you will perform machine learning tasks. You are given the choice of three notebooks, each of which leverages a different type of algorithm.
This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.
