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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-trading-machine-learning-gcp
课程评论：没有评论
课程名称：交易、机器学习和谷歌云平台（GCP）入门 课程概述：在本课程中，您将学习交易的基本知识，包括趋势、收益、止损和波动性等概念。您将掌握识别利润来源和构建基本量化交易策略的结构。本课程将帮助您评估模型的泛化能力，解释回归与预测之间的区别，并识别开发和实施回测所需的步骤。课程结束时，您将能够使用谷歌云平台在Jupyter Notebook中构建基本的机器学习模型。 成功参与本课程，您需要具备高级的Python编程能力，并熟悉与机器学习相关的库，例如Scikit-Learn、StatsModels和Pandas。同时，SQL经验也被推荐。您还应具备统计学背景（期望值和标准差、高斯分布、高阶矩、概率、线性回归）以及金融市场的基础知识（股票、债券、衍生品、市场结构、对冲）。 课程大纲： 1. **交易与机器学习导论** - 描述：本模块将向您介绍交易的基本知识和机器学习的概念。机器学习既是一门艺术，需要掌握参数组合以产生准确且泛化的模型；同时也是一门科学，涉及解决特定问题的理论知识。 2. **使用BigQuery ML的监督学习** - 描述：本模块将介绍监督机器学习及其在交易问题中常用的一些算法。您将获得使用BigQuery Machine Learning构建回归模型的实践经验。 3. **时间序列和ARIMA建模** - 描述：本模块将教授ARIMA建模及其在时间序列数据中的应用。您将获得为金融数据集构建ARIMA模型的实践经验。 4. **神经网络与深度学习导论** - 描述：本模块将介绍神经网络及其与深度学习的关系。您还将学习如何通过正则化和交叉验证来评估模型的泛化能力。此外，您将了解谷歌云平台（GCP），并学习如何利用GCP实施交易技术。
Name:Introduction to Trading with Machine Learning on Google Cloud
Description:In this module you will be introduced to the fundamentals of trading. You will also be introduced to machine learning. Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of problems.
Name:Supervised Learning with BigQuery ML
Description:In this module you will be introduced to supervised machine learning and some relevant algorithms commonly applied to trading problems. You will get some hands-on experience building a regression model using BigQuery Machine Learning
Name:Time Series and ARIMA Modeling
Description:In this module you will learn about ARIMA modeling and how it is applied to time series data. You will get hands-on experience building an ARIMA model for a financial dataset.
Name:Introduction to Neural Networks and Deep Learning
Description:In this module you'll learn about neural networks and how they relate to deep learning. You'll also learn how to gauge model generalization using regularization, and cross-validation. Also, you'll be introduced to Google Cloud Platform (GCP). Specifically, you'll be shown how to leverage GCP for implementing trading techniques.
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. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
