In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
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课程信息
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
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体验 Coursera 企业版您将学到的内容有
Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.
Define quantitative trading and the main types of quantitative trading strategies.
Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.
Understand the application of machine learning to financial use cases.
您将获得的技能
- Finance
- Trading
- Investment
- Machine Learning applied to Finance
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
对员工进行热门技能培训能否为您的公司带来益处?
体验 Coursera 企业版授课大纲 - 您将从这门课程中学到什么
Introduction to Trading with Machine Learning on Google Cloud
Supervised Learning with BigQuery ML
Time Series and ARIMA Modeling
Introduction to Neural Networks and Deep Learning
审阅
- 5 stars44.18%
- 4 stars30.23%
- 3 stars14.47%
- 2 stars4.39%
- 1 star6.71%
来自INTRODUCTION TO TRADING, MACHINE LEARNING & GCP的热门评论
Some of the content in Week 4, might be better placed earlier in the course. Other than that it was a great learning experience.
Good course that gives a lot of breadth as an introduction to machine learning in finance. Well put together
Excellent! But, I am missing some of the prerequisites since I just wanted to take a chance and try things out, but feel like proceeding further might lead to some stumbling blocks.
Not as much coding as I would have wanted, or atleast exposure to code. Very solid historical context though.
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