The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

# Machine Learning and Reinforcement Learning in Finance 专项课程

Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.

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## 关于此 专项课程

### 您将学到的内容有

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

Describe linear regression and classification models and methods of their evaluation

Explain how Reinforcement Learning is used for stock trading

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

### 您将获得的技能

#### 可分享的证书

#### 100% 在线课程

#### 灵活的计划

#### 中级

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

#### 完成时间大约为5 个月

#### 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。

### 此专项课程包含 4 门课程

### Guided Tour of Machine Learning in Finance

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.

### Fundamentals of Machine Learning in Finance

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.

### Reinforcement Learning in Finance

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.

### Overview of Advanced Methods of Reinforcement Learning in Finance

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.

### 关于 纽约大学坦登工程学院

### 审阅

#### 3.8

##### 来自MACHINE LEARNING AND REINFORCEMENT LEARNING IN FINANCE的热门评论

Great refreshment on Stochastic calculus and overall rewind of the specialization!

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.

Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.

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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。

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此课程是 100% 在线学习吗？是否需要现场参加课程？

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。

完成专项课程后我会获得大学学分吗？

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。

What background knowledge is necessary?

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.

还有其他问题吗？请访问 学生帮助中心。