What Managers Need To Know About Machine Learning


Dr. Hugo Bowne-Anderson, data scientist, educator, and host of the podcast DataFramed, tells you everything you always wanted to know about machine learning but were too afraid to ask. You'll understand the basic concepts in machine learning, how they can apply to business problems, when to use them and when not to use them, and how to talk the talk with data scientists. 

Grounded in real-world examples from finance, healthcare tech, and other verticals, Hugo demystifies the main components of machine learning. You’ll come out knowing what unsupervised, supervised and reinforcement learning are and when to use them. You’ll even know what logistic regression, principal component analysis, and k-fold cross validation are. The language of machine learning will soon be a central part of any decision maker’s toolkit. Stay ahead of the curve and learn it now. 

You can find the slides here.

Hugo Bowne-Anderson

Head of Data Science Evangelism and Marketing at Coiled; Previously a Data Scientist at DataCamp

Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science seamlessly. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at DataCamp, the online education platform for all things data. He has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open-source software, both for individuals and the enterprise.

Connect with Hugo on LinkedIn and Twitter.

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