This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
New York University
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- 5 stars41.46%
- 4 stars24.24%
- 3 stars13.23%
- 2 stars11%
- 1 star10.04%
來自GUIDED TOUR OF MACHINE LEARNING IN FINANCE的熱門評論
The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
Homework is not always consistent with what's covered in class. The recommended readings are very helpful.
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
關於 Machine Learning and Reinforcement Learning in Finance 專項課程
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.