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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/build-regression-classification-clustering-models
课程评论：没有评论
课程名称：构建回归、分类和聚类模型 概览：在大多数机器学习项目中，最终目标是产生一个模型。模型能够做出决策和预测，帮助企业比人类更好地理解自身、客户及其环境。本课程是认证人工智能从业者（CAIP）专业证书的第三门课程，介绍了一些用于解决常见监督学习问题（回归和分类）及常见无监督学习问题（聚类）的主要机器学习算法。学员将通过之前课程学习的机器学习工作流程，建立多个模型，解决每种问题。 课程大纲： 1. **使用线性代数构建线性回归模型**：深入机器学习算法，了解如何选择合适的算法，并应用于解决问题，从简单线性回归入手。 2. **构建正则化和迭代线性回归模型**：学习如何通过正则化提高线性回归模型的性能，以及应用迭代方法以克服封闭形式解的不足。 3. **训练分类模型**：训练二分类模型，了解多分类模型，掌握不同算法的特点及适用场景。 4. **评估和调整分类模型**：通过评估模型性能和调整参数，提升分类模型的精确度。 5. **构建聚类模型**：在无监督学习中，学习如何通过模型识别未标记数据中的有用模式。 6. **应用所学知识**：通过项目实践，将课程内容应用于实际场景，巩固掌握的知识。 本课程的目标是技术性地探索各种机器学习算法，帮助学员构建解决问题的模型。
Name:Build Linear Regression Models Using Linear Algebra
Description:In the preceding course, you went through the overall machine learning workflow from start to finish. Now it's time to start digging into the algorithms that make up machine learning. This will help you select the most appropriate algorithm(s) for your own purposes, as well as how best to apply them to solve a problem. A good place to start is with simple linear regression.
Name:Build Regularized and Iterative Linear Regression Models
Description:The simple model you created earlier works well in many cases, but that doesn't mean it's the optimal approach. Linear regression can be enhanced by the process of regularization, which will often improve the skill of your machine learning model. In addition, an iterative approach to regression can take over where the closed-form solution falls short. In this module, you'll apply both techniques.
Name:Train Classification Models
Description:Besides linear regression, the other major type of supervised machine learning outcome is classification. To begin with, you'll train some binary classification models using a few different algorithms. Then, you'll train a model to handle cases in which there are multiple ways to classify a data example. Each algorithm may be ideal for solving a certain type of classification problem, so you need to be aware of how they differ.
Name:Evaluate and Tune Classification Models
Description:It's not enough to just train a model you think is best, and then call it a day. Unless you're using a very simple dataset or you get lucky, the default parameters aren't going to give you the best possible model for solving the problem. So, in this module, you'll evaluate your classification models to see how they're performing, then you'll attempt to improve their skill.
Name:Build Clustering Models
Description:You've built models to tackle linear regression problems and classification problems. One of the other major machine learning tasks that you might want to engage in is clustering, a form of unsupervised learning. In this module, you'll see how a machine learning model can help you identify useful patterns even when the data you have to work with isn't labeled.
Name:Apply What You've Learned
Description:You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.
In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business. This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course. Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
