× Coursera - Machine Learning for Trading Specialization by Google Cloud Close
Coursera - Machine Learning for Trading Specialization by Google Cloud
Video: .mp4 (1280x720) | Audio: AAC, 44100 kHz, 2ch | Size: 1.48 Gb
Genre: eLearning Video | Duration: 9h 14m | Language: English
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.
Introduction to Trading, Machine Learning & GCP
This course is for finance professionals, investment management professionals, and traders. Alternatively, this course can be for machine learning professionals who seek to apply their craft to trading strategies.
At the end of the course you will be able to do the following: - Understand the fundamentals of trading, including the concept of trend, returns, stop-loss and volatility - Understand the differences between supervised/unsupervised and regression/classification machine learning models - Identify the profit source and structure of basic quantitative trading strategies - Gauge how well the model generalizes its learning - Explain the differences between regression and forecasting - Identify the steps needed to create development and implementation backtesters - Use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks To be successful in this course, 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).
Using Machine Learning in Trading and Finance
This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
At the end of the course you will be able to do the following: - Design basic quantitative trading strategies - Use Keras and Tensorflow to build machine learning models - Build a pair trading strategy prediction model and back test it - Build a momentum-based trading model and back test it To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library. 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).
Reinforcement Learning for Trading Strategies
This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
At the end of the course you will be able to do the following: - Understand what reinforcement learning is and how trading is an RL problem - Build Trading Strategies Using Reinforcement Learning (RL) - Understand the benefits of using RL vs. other learning methods - Differentiate between actor-based policies and value-based policies - Incorporate RL into a momentum trading strategy To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library.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).
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