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Develop practical understanding of machine learning techniques for investment processes, covering algorithms, performance evaluation, and ethical considerations tailored for industry professionals.
Learn essential data analysis and statistical techniques for investment professionals, including basic measures, distributions, visualization with Python, sampling theory, and hypothesis formulation for investment problems.
Explore foundational statistics for machine learning in investments, covering probability, regression, and time-series models. Learn Python and R programming for efficient decision-making in finance.
Develop practical machine learning skills for investment analysis, including data visualization, probability concepts, time-series models, and ethical decision-making in finance.
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