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In this episode of the DATAcated podcast, host Kate Strachnyi talks with Stefan Jansen about machine learning for algorithmic trading. Stefan has been a partner in an investment firm where he assisted in building data infrastructure and predictive analytics practice. He accomplished this when data science was only beginning to be taken seriously in the investment industry. You won’t want to miss this opportunity to learn from Stefan’s experiences.

You will want to hear this episode if you are interested in... * What is machine learning in trading? [02:53] * The purpose behind Stefan’s book [05:29] * Personal finance and personal investments [11:58] * Stefan’s best trade or investment idea [13:21] * Choosing Python vs. C [17:57] * The third edition and what to expect [21:27] * Pros and cons of algorithmic trading [29:54] * Expectations in the algorithmic trading space [37:48]

Resources & People Mentioned * Machine Learning for Algorithmic Trading * Quandl * Bryan Kelly * Bloomberg.com * AI 2041: Ten Visions for Our Future * GitHub * Machine Learning for Trading * Machine Learning for Trading Community

Connect with Stefan Jansen * On LinkedIn * On Twitter

Connect with DATAcated * http://www.datacated.com/ * DATAcated on LinkedIn: https://www.linkedin.com/company/datacated1/ * Kate on LinkedIn: https://www.linkedin.com/in/kate-strachnyi-data/ * DATAcated on Twitter: https://twitter.com/datacated_ * DATAcated on YouTube: https://www.youtube.com/datacated

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