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Machine LearningFinanceTradingInvestment

100% 在線

立即開始,按照自己的計劃學習。

第 1 門課程(共 3 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為14 小時

建議:19 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

Introduction to Trading, Machine Learning and GCP

13 個視頻 (總計 57 分鐘), 1 個閱讀材料, 3 個測驗
13 個視頻
Trading vs Investing6分鐘
The Quant Universe2分鐘
Quant Strategies7分鐘
Quant Trading Advantages and Disadvantages4分鐘
Exchange and Statistical Arbitrage8分鐘
Index Arbitrage2分鐘
Statistical Arbitrage Opportunities and Challenges5分鐘
Introduction to Backtesting5分鐘
Backtesting Design6分鐘
What is AI and ML ? What is the difference between AI and ML?58
Applications of ML in the Real World1分鐘
What is ML?3分鐘
1 個閱讀材料
Welcome to Introduction to Trading, Machine Learning and GCP10分鐘
3 個練習
Introduction to Trading5分鐘
Python Skills Assessment Quiz
Intro to AI and ML5分鐘
2
完成時間為 3 小時

Supervised Learning and Forecasting

13 個視頻 (總計 72 分鐘), 3 個測驗
13 個視頻
Regression and classification11分鐘
Short history of ML: Linear Regression7分鐘
Short history of ML: Perceptron5分鐘
Lab Intro: Building a Regression Model37
Introduction to Qwiklabs3分鐘
Lab Walkthrough: Building a Regression Model9分鐘
What is forecasting? - part 15分鐘
What is forecasting? - part 24分鐘
Choosing the right model and BQML - part 13分鐘
Choosing the right model and BQML - part 22分鐘
Lab Intro: Forecasting Stock Prices using Regression in BQML36
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12分鐘
1 個練習
Forecasting
3
完成時間為 2 小時

Time Series and ARIMA Modeling

11 個視頻 (總計 52 分鐘), 2 個測驗
11 個視頻
AR - Auto Regressive7分鐘
MA - Moving Average2分鐘
The Complete ARIMA Model4分鐘
ARIMA compared to linear regression7分鐘
How can you get a variety of models from just a single series?1分鐘
How to choose ARIMA parameters for your trading model4分鐘
Time Series Terminology: Auto Correlation4分鐘
Sensitivity of Trading Strategy4分鐘
Lab Intro: Forecasting Stock Prices Using ARIMA32
Lab Walkthrough: Forecasting Stock Prices using ARIMA7分鐘
1 個練習
Time Series
4
完成時間為 1 小時

Introduction to Neural Networks and Deep Learning

9 個視頻 (總計 36 分鐘), 3 個測驗
9 個視頻
Short history of ML: Modern Neural Networks8分鐘
Overfitting and Underfitting6分鐘
Validation and Training Data Splits4分鐘
Why Google?1分鐘
Why Google Cloud Platform?2分鐘
What are AI Platform Notebooks1分鐘
Using Notebooks1分鐘
Benefits of AI Platform Notebooks2分鐘
3 個練習
Model generalization
Google Cloud
Module Quiz8分鐘
3.8
60 條評論

來自Introduction to Trading, Machine Learning & GCP的熱門評論

創建者 AAJan 13th 2020

Good course that gives a lot of breadth as an introduction to machine learning in finance. Well put together

創建者 CRJan 2nd 2020

Other courses recommended before doing this one! Basics of ML, Basics of the stock market, python and sql

講師

授課教師 Jack Farmer 的圖片

Jack Farmer

Curriculum Director
New York Institute of Finance
授課教師 Ram Seshadri 的圖片

Ram Seshadri

Machine Learning Consultant
Google Cloud Platform

關於 Google 云端平台

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

關於 New York Institute of Finance

The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks....

關於 Machine Learning for Trading 專項課程

This Specialization is for finance professionals, including but not limited to: hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies. The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create long-term trading strategies, short-term trading strategies, and hedging strategies. To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. 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)....
Machine Learning for Trading

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