Python and ML for Asset Management

Python and ML for Asset Management

(0 reviews)
Share icon

The course encompasses the fundamentals of supervised and unsupervised machine learning for financial data, as well as logistic regression, classification algorithms, investment management models, and practical implementation using Python.

Key AI Functions:Computer Science,Investment management knowledge,Expertise in data science,Programming skills,Managing your own personal invetsments

Description for Python and ML for Asset Management

Features of Course

  • Acquire an understanding of the fundamentals of supervised and unsupervised machine learning techniques as they pertain to financial data sets.
  • Comprehend the fundamental principles of logistical regression and machine learning algorithms for the classification of variables into one of two possible outcomes.
  • Implement machine learning algorithms in case studies by leveraging robust Python libraries.
  • Discover the application of factor models and regime transition models in investment management.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by EDHEC Business School

    Duration: 16 hours (approximately)

    Schedule: Flexible

    Reviews for Python and ML for Asset Management

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for Python and ML for Asset Management

    Gain a fundamental understanding of machine learning technologies, data impact, training models on non-programming platforms, and form an informed perspective on its societal implications.

    #Artificial Intelligence (AI) #Computer Science
    icon

    Learn to create and diversify portfolio strategies, apply machine learning to financial data, and utilize quantitative modeling and data analytics for investment decisions.

    #Risk Management #Portfolio construction and analysis
    icon

    The course encompasses the following topics: the development of a text processing pipeline, the comprehension of Naive Bayes classifier theory, and the assessment of the efficacy of classification models following training.

    #Computer Science #Machine Learning
    icon