This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Investment Management with Python and Machine Learning
Über diesen Kurs
Kompetenzen, die Sie erwerben
- 5 stars23,64 %
- 4 stars15,20 %
- 3 stars25 %
- 2 stars18,58 %
- 1 star17,56 %
Top-Bewertungen von PYTHON AND MACHINE LEARNING FOR ASSET MANAGEMENT
Good overview on Machine Learning techniques, need for some basic knowledge in statistics and Python for an optimized experience.
Very nice course sharing many types of knowledges around data / cleaning / type of data / several algorithms / organised Python coding
The topics covered in this course are really interesting. I learned a great deal by studying various papers covered in this course - Thank you to both instructors!
would be good to focus more on the jupyter notebooks and less on multiple choice. Really interesting notebooks and quite advanced / technical material which deserves more time and coverage.
Über den Spezialisierung Investment Management with Python and Machine Learning
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.
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