Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This article is written by Berend Gort & Bruce Yang, core team members of the Open-Source project AI4Finance.

Numerai is a crowdsourced fund, a hedge fund that operates based on the results of stock price predictions made by an unspecified number of people. Numerai holds tournaments in which participants compete for forecasting performance. Numerai is a crowdsourced fund, a hedge fund that operates based on the results of stock price predictions made by an unspecified number of people. Numerai holds tournaments in which participants compete for forecasting performance. Tournament participants will build a predictive model based on an encrypted dataset provided by Numerai, and then use it to create a submission.

Here are 5 books I've read recently that I thought were excellent and definitely worth any AI person's time… One of the books isn't even on AI, but I think the topic should be required for many practitioners and thought leaders. I first read this book a few years ago when it was published, and I find myself coming back to it as a reference every few months when I need to make sure I'm understanding a concept or need to double check mine or someone else's work. It's an excellent summary to theory and concepts for someone who wants a little deeper understanding. I think a great route to learning and getting a moderately deep theoretical and practical understanding is to go piece by piece through this text and the next text "Deep Learning with Python" simultaneously. Try to push through a chapter of theory and understand at least the basics of the chapter in "Deep Learning" and then go build these ideas using a corresponding chapter from "Deep Learning with Python."

In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today's high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach -- his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field.

Machine learning is a buzzword often thrown about when discussing the future of finance and the world. You may have heard of neural networks solving problems in facial recognition, language processing, and even financial markets, yet without much explanation. It is easy to view this field as a black box, a magic machine that somehow produces solutions, but nobody knows why it works. It is true that machine learning techniques (neural networks in particular) pick up on obscure and hard to explain features, however there is more room for research, customization, and analysis than may first appear. Today we'll be discussing at a high level the various factors to be considered when researching investing through the lens of machine learning. The contents of this notebook and further discussions on this topic are heavily inspired by Marcos Lopez de Prado's book Advances in Financial Machine Learning.