Goldman Sachs MD's guide to top machine learning courses

If you've started this year with aspirations to further your knowledge of such things as Python coding and machine learning, but you happen to have a full time job that demands most of your time, do not be discouraged. 

One Goldman Sachs MD says he completed eight online training courses last year, while also fulfilling his role as head of engineering (strats, quants and technologists) in EMEA and APAC for the private investments arm of Goldman Sachs Asset Management. 

In a post on LinkedIn, Dr Andrew McLennan says took the following courses in 2021 and that they helped keep his technical and leadership skills up to date. Some of these we've highlighted before. Some are new. Not all are machine learning-focused, but the first four in particular are directly relevant.

1. Machine Learning, by Stanford University and Coursera. 
2. Deep Learning Specialization, by DeepLearning.AI
3. AWS Fundamentals Specialization, by aws.amazon.com
4. Google Cloud Digital Leader Specialization, by cloud.google.com
5. Scalability & System Design for Developers, by Educative.IO
6. Python for Programmers, by Educative.IO
7. Agile and JIRA, by atlassian.com
8. Managing Remote Teams, by gitlab.com

McLennan manages teams analyzing the investment process using AWS, Google Cloud and machine learning techniques. He has a PhD from Oxford University in medical image analysis and information engineering, codes in Python and Goldman's proprietary trading language, Slang, and has been an MD since 2019, but clearly felt that his skills could benefit from being refreshed. 

McLennan's advice on online courses comes after Goldman posted a new developer blog about the use of machine learning in its data lake. Among other things, it stresses the importance of explainable models. "For Machine Learning Engineers, it can become a full day's work to explain why a certain prediction was made by the model," says author Jaimita Bansal, a VP in data lake engineering. Machine learning visualizations have become key to model explainability, said Bansal. McLennan may want to study these next. 

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