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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-for-finance/
课程评论：没有评论
课程名称：金融领域的人工智能（AI for Finance） 课程概述：在金融行业中，许多关键问题的解决方案依赖于预测未来的数据模式，以便做出更好的即时财务决策。近年来，现代机器学习方法和工具的演变为计算机视觉领域带来了希望，同样的进展也有可能在金融预测等重要领域实现。在本课程中，您将首先学习如何快速入门金融领域的机器学习，通过使用简单的现代机器学习方法来预测未来的汇率。在这个示例中，您将学习如何选择基本的数据准备方法和模型，然后再学习如何改进它们。在接下来的模块中，您将发现多种数据准备方法，并了解它们如何影响模型训练的准确性。最后一模块，您将学习如何寻找和测试几种关键的现代机器学习模型，从中选择表现最好的一个。完成本课程后，您将对如何将机器学习方法应用于金融数据预测有一个扎实的入门认识。 关于作者：Jakub Konczyk 自1995年以来一直热衷于编程并从事专业编程工作。他是Python和Django方面的专家，自2006年以来参与构建复杂系统。Jakub 喜欢简化并教授编程相关主题，并与他人分享。他第一次接触机器学习是在尝试预测某初创企业的房地产价格时，但结果非常失败。后来，他发现了一种更实用的学习机器学习的方法，并希望在本课程中与您分享。这种方法的核心是“保持简单！”的信念。
A lot of solutions to key problems in the financial world require predicting the future patterns in data from the past to make better financial decisions right now. The evolution of modern machine learning methods and tools in recent years in the field of computer vision bring promise of the same progress in other important fields such as financial forecasting.In this course, you'll first learn how to quickly get started with ML in finances by predicting the future currency exchange rates using a simple modern machine learning method. In this example, you'll learn how to choose the basic data preparation method and model and then how to improve them. In the next module, you'll discover a variety of ways to prepare data and then see how they influence models training accuracy. In the last module, you'll learn how to find and test a few key modern machine learning models to pick up the best performing one.After finishing this course, you'll have a solid introduction to apply ML methods to financial data forecasting.About The AuthorJakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to "Keep it simple!" mantra.
