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Coursera - Investment Management with Python and Machine Learning Specialization
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Coursera - Investment Management with Python and Machine Learning Specialization
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 35h | Size: 6.9 GB

WHAT YOU WILL LEARN
Write custom Python code and use existing Python libraries to build and analyse efficient portfolio strategies.

Write custom Python code and use existing Python libraries to estimate risk and return parameters, and build better diversified portfolios.

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

Gain an understanding of advanced data analytics methodologies, and quantitative modelling applied to alternative data in investment decisions

SKILLS YOU WILL GAIN
Risk Management
Portfolio construction and analysis
Python programming skills
Implementation of data science techniques in investment decisions
Portfolio Optimization
Programming skills
Managing your own personal invetsments
Investment management knowledge
Computer Science
Expertise in data science
Advanced vizualisation
Basics of consuption-based alternative data

About this Specialization
16,428 recent views
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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