关于此 专项课程
100% 在线课程

100% 在线课程

立即开始,按照自己的计划学习。
灵活的计划

灵活的计划

设置并保持灵活的截止日期。
中级

中级

Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.

完成时间(小时)

完成时间大约为5 个月

建议 9 小时/周
可选语言

英语(English)

字幕:英语(English)...

您将学到的内容有

  • Check

    Compare ML for Finance with ML in Technology (image and speech recognition, robotics, etc.)

  • Check

    Describe linear regression and classification models and methods of their evaluation

  • Check

    Explain how Reinforcement Learning is used for stock trading

  • Check

    Become familiar with popular approaches to modeling market frictions and feedback effects for option trading.

您将获得的技能

Predictive ModellingFinancial EngineeringMachine LearningTensorflowReinforcement Learning
100% 在线课程

100% 在线课程

立即开始,按照自己的计划学习。
灵活的计划

灵活的计划

设置并保持灵活的截止日期。
中级

中级

Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.

完成时间(小时)

完成时间大约为5 个月

建议 9 小时/周
可选语言

英语(English)

字幕:英语(English)...

专项课程 的运作方式

加入课程

Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习,请直接注册专项课程,或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时,您将自动订阅整个专项课程。您可以只完成一门课程,您可以随时暂停学习或结束订阅。访问您的学生面板,跟踪您的课程注册情况和进度。

实践项目

每个专项课程都包括实践项目。您需要成功完成这个(些)项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程,则需要在开始之前完成其他所有课程。

获得证书

在结束每门课程并完成实践项目之后,您会获得一个证书,您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。

how it works

此专项课程包含 4 门课程

课程1

Guided Tour of Machine Learning in Finance

3.6
198 个评分
82 个审阅
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....
课程2

Fundamentals of Machine Learning in Finance

3.5
83 个评分
19 个审阅
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....
课程3

Reinforcement Learning in Finance

3.4
27 个评分
9 个审阅
This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable....
课程4

Overview of Advanced Methods of Reinforcement Learning in Finance

3.6
16 个评分
2 个审阅
In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high frequency trading, cryptocurrencies, peer-to-peer lending, and more....

关于 New York University Tandon School of Engineering

Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship....

常见问题

  • 可以!点击您感兴趣的课程卡开始注册即可。注册并完成课程后,您可以获得可共享的证书,或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分,系统会自动为您订阅完整的专项课程。访问您的学生面板,跟踪您的进度。

  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • 此专项课程不提供大学学分,但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。

  • Prerequisites for the specialization are basic math including calculus and linear algebra, basic probability theory and statistics, and some programming skills in Python. For students that are not familiar with Python and IPython / Jupyter notebooks, reference to tutorials are provided as a part of further reading.

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