This course covers AI fundamentals in finance, Python basics, financial libraries (Numpy, Pandas, etc.), and SQL. It includes machine learning concepts, supervised/unsupervised learning, reinforcement learning, and algorithmic trading, concluding with a real-world case study application.
Understand how to apply the fundamentals of machine learning to AI, finance and technology
Determine how to utilize algorithmic trading programs
Explore what programs work well in financial technology
Reinforce skills in regression and equity analysis
About this course:
This course covers AI fundamentals and concepts as pertaining to finance. The course will review python programming basics such as Basic Input-Output Operations, Basic Operators, Conditional Execution, Loops, and Lists. The focus will be on how to utilize financial libraries for python including the use of Numpy, Pandas, Pyalgotrade, FinmarketPy and Scipy. SQL a domain-specific language used in programming and designed for managing data will also be utilized in projects and case studies.The course will look at machine learning, the mathematical foundations for machine learning. There will be extensive work with supervised learning, unsupervised learning, reinforcement learning and algorithmic trading concepts.The course will conclude with a case study applying one of these concepts to a real-world situation.
This course may be applicable to select computer science certificate programs as an elective.
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