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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/build-decision-trees-svms-neural-networks
课程评论：没有评论
课程名称：构建决策树、支持向量机和人工神经网络 课程概述：本课程旨在介绍多种机器学习算法的特点，帮助学员选择最适合特定问题的解决工具。决策树和支持向量机（SVM）是可以解决回归和分类问题的两种算法，但它们的应用场景不同。更高级的机器学习方法——深度学习，利用人工神经网络（ANN）解决更复杂的问题。本课程是认证人工智能从业者（CAIP）专业证书的第四门也是最后一门课程，深入探讨了多种用于机器学习和深度学习的算法。学员将在实际工作流中构建多个可解决商业问题的模型，最终完成对不同机器学习算法的技术探索。 课程大纲： 1. **构建决策树和随机森林**：介绍决策树和随机森林算法，帮助学员构建用于解决回归和分类问题的机器学习模型。 2. **构建支持向量机（SVM）**：学习如何使用SVM处理异常值和高维数据，构建高效的回归和分类模型。 3. **构建多层感知器（MLP）**：在机器学习的基础上，深入到深度学习的子领域，构建基础的多层感知器（MLP），能处理更复杂的数据任务。 4. **构建卷积和递归神经网络（CNN/RNN）**：将所学的MLP网络应用于更广泛的架构中，学习解决计算机视觉问题的卷积神经网络（CNN）和处理自然语言的递归神经网络（RNN）。 5. **应用所学知识**：通过项目将课程内容应用于实际场景，以巩固所学知识。 本课程的目标是帮助学员掌握多种机器学习算法，提升解决问题的能力。
Name:Build Decision Trees and Random Forests
Description:You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.
Name:Build Support-Vector Machines (SVM)
Description:Another alternative approach to regression and classification comes in the form of support-vector machines (SVMs). In this module, you'll build SVMs that can do a good job of handling outliers and tackling high-dimensional data in an efficient manner.
Name:Build Multi-Layer Perceptrons (MLP)
Description:All of the algorithms discussed thus far fall under the general umbrella of machine learning. While they are powerful and complex in their own right, the algorithms that make up the subdomain of deep learning—called artificial neural networks (ANNs)—are even more so. In this module, you'll build a fundamental version of an ANN called a multi-layer perceptron (MLP) that can tackle the same basic types of tasks (regression, classification, etc.), while being better suited to solving more complicated and data-rich problems.
Name:Build Convolutional and Recurrent Neural Networks (CNN/RNN)
Description:Now that you've built MLP neural networks, you can incorporate them into two wider architectures: convolutional neural networks (CNNs), which excel at solving computer vision problems; and recurrent neural networks (RNNs), which are most often used to process natural languages.
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 a practical scenario.
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job. This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
