このコースについて

15,104 最近の表示
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

約12時間で修了
英語
字幕:英語

習得するスキル

Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

約12時間で修了
英語
字幕:英語

提供:

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ニューヨーク金融金融研究所

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Google Cloud

シラバス - 本コースの学習内容

1

1

3時間で修了

Introduction to Course and Reinforcement Learning

3時間で修了
10件のビデオ (合計64分), 1 reading, 1 quiz
10件のビデオ
What is Reinforcement Learning?9 分
History Overview2 分
Value Iteration9 分
Policy Iteration6 分
TD Learning8 分
Q Learning6 分
Benefits of Reinforcement Learning in Your Trading Strategy6 分
DRL Advantages for Strategy Efficiency and Performance7 分
Introduction to Qwiklabs3 分
1件の学習用教材
Idiosyncrasies and challenges of data driven learning in electronic trading10 分
2

2

5時間で修了

Neural Network Based Reinforcement Learning

5時間で修了
9件のビデオ (合計39分)
9件のビデオ
Deep Q Networks - Loss2 分
Deep Q Networks Memory2 分
Deep Q Networks - Code3 分
Policy Gradients4 分
Actor-Critic3 分
What is LSTM?7 分
More on LSTM4 分
Applying LSTM to Time Series Data7 分
3

3

4時間で修了

Portfolio Optimization

4時間で修了
10件のビデオ (合計54分)
10件のビデオ
Steps Required to Develop a DRL Strategy7 分
Final Checks Before Going Live with Your Strategy5 分
Investment and Trading Risk Management4 分
Trading Strategy Risk Management4 分
Portfolio Risk Reduction4 分
Why AutoML?13 分
AutoML Vision2 分
AutoML NLP3 分
AutoML Tables7 分

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REINFORCEMENT LEARNING FOR TRADING STRATEGIES からの人気レビュー

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Machine Learning for Trading専門講座について

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) 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 (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended....
Machine Learning for Trading

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