Machine Learning and Finance by NYU

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Skills Covered: Machine Learning for Financial Engineering, Deep Learning for Financial Engineering, Neural Networks or Financial Engineering, Supervised Learning, Linear regression, Regression Loss Function, Bias and Variance, Transformations, Classification, Error Analysis, Decision Trees, Naive Bayes, Ensembles, Support Vector Machines, Gradient Descent, Unsupervised Learning, Dimensionality Reduction, Clustering, Convolutional Neural Networks, Recurrent Neural Networks, Training Neural Networks, Transfer Learning, Neural Language Processing (NLP)

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Competitive organizations need teams who can leverage massive and complex datasets, deriving insights that are strategic and actionable. In the financial sector, machine learning has emerged as one of the most critical tools for decision-making. Whether it’s streamlining operations, informing investment decisions, or assessing risk, finance professionals with machine learning skills that improve decision making will have a discernible competitive advantage.

In this professional certificate program, you will learn the key skills for constructing machine learning models, and using data to inform decisions. Whether you are a trader, financial analyst or programmer; whether your focus is on portfolio management or quantitative analytics, you will acquire the skills to apply both Classical Machine Learning and Neural Network/Deep Learning solutions to problems in finance. As important: you will learn a systematic approach to problem solving through data analysis, increasing your value in the emerging data-driven world. Combined with both theory and practical advice, you will be well positioned for solving the supervised and unsupervised learning tasks that will be critically important to all organizations.

The urgent demand for machine learning in finance is only going to grow. But the skills you will develop in this program are key to decision making in many other domains as well. Having these skills will enhance your value in many industries and will be invaluable to your career.



  • Develop a strong understanding of common applications of machine learning in finance
  • Learn how to successfully use Classical Machine Learning techniques like Regression and Classification
  • Identify neural networks and deep learning techniques and architectures and their applications in finance
  • Build a deeper understanding of supervised learning (regression and classification) and unsupervised learning, and the appropriate applications of both
  • Construct machine learning models to solve practical problems in finance



  • According to Burning Glass Labor Insights, there were over 150,000 jobs in the U.S. between 2019-2020 for Financial Analysts, with projected growth of 10% over the next 10 years
  • Financial technologies are becoming increasingly valuable particularly for companies that operate in areas like insurance, payments, asset management, and personal finance management, and these skills will be even more relevant in the next few years


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Machine Learning and Finance by NYU
Machine Learning and Finance by NYU


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