Introduction to AI and data science
Terms and definitions: What does machine learning mean?
Historical context and present day
Drivers for AI and data science
What’s so different about big data?
AI is eating the world.
Making AI practical
Algorithms and techniques
Data formats, databases, and schemas
Evaluating model performance and validating models
Terminology: Regression, classification, supervised, and unsupervised
Advanced models: Random forests, support vector machines, deep learning, and neural networks
Industry use cases
AI within the organization
Maturity levels for AI
Evaluating good projects for AI
Build versus buy and hire versus train
Skills, tools, and platforms needed for AI
Structuring data and AI initiatives within your organization: Successful and cautionary tales
Common pitfalls and fallacies in AI and data science
AI and data science in the headlines: The good, the bad, and the ugly
Legal and regulatory implications
Litigation and liabilities of bad data science
Common fallacies in data science and AI
Lying with statistics and how to spot it
Michael Li is the founder of the Data Incubator, an elite fellowship program that trains and places data scientists and quants with advanced degrees (PhD or masters) into industry roles. Previously, Michael was a data science lead with Foursquare and with Andreessen Horowitz. He holds PhD in math from Princeton University.
Russell Martin is a data scientist in residence at the Data Incubator, where he instructs fellows, teaches online courses, and leads training courses with corporate partners. Russ lived and worked in the UK for 17 years, including at Warwick University and the University of Liverpool, where he taught in the Department of Computer Science. He holds a PhD in applied mathematics from the Georgia Institute of Technology.
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