For those who want to take the extended deep dive on Machine Learning, there are some good MOOCs , like Andrew Ng's course at Stanford or Pedro Domingos' Machine Learning course out of the University of Washington. It should be noted that one thing is missing from both of these courses - the busines case -- how does this fit within a team? What's the process for engineering, using predictive analytics / predictive modeling. How do you deploy machine learning apps into something that brings revenue?
This course will focus more on the business / use cases.
The course begins with a history -- all the way back to the Battle of London, the precursors of cybernetics, from early work in adaptive signal processing to Neural Networks then to Genetic Programming, early AI systems, and ultimately back through Neural Networks.
How much math is needed for the class? Basic linear algebra / matrix multiplication / pre-calc-level analytic geometry. That's all.
We'll discuss how to apply advanced math for business use cases, leveraging open source frameworks for Big Data. This is about the kind of math that Twitter and others employ for their revenue apps, along with the how's and why's — all readily accessible even if you didn't take several years of Calculus. We'll focus on concrete business use cases, historical context, and brief code examples in R and Python. We'll also focus on case studies and expert advice about the soft skills required to put these techniques into practice, the bring the returns on investment.
* historical context for machine learning: what were the innovators trying to solve and how might you apply that in your business use cases?
* defining the terminology: fitting the terms into a cohesive whole picture
* "Just Enough Math": advanced math for business people, to leverage open source frameworks for Big Data — all based on concrete business use cases and brief code samples
* soft skills: building teams, process for deploying ML apps at scale
* how to evaluate projects quantitatively, feature selection, tournaments, etc.
* compare/contrast of popular algorithms, shown in R and Python, reviewing the trade-offs for different model selection
* survey of available open source frameworks for building data workflows in a team context, with a scorecard indicating which fit best for particular use cases
* case study: building a recommender systems, leveraging Open Data + Big Data
Paco Nathan @pacoid, is a “player/coach” who's led innovative Data teams building large-scale apps for 10+ years. Expert in distributed systems, machine learning, Enterprise data workflows. Paco is an O'Reilly author, and an advisor for several firms including The Data Guild, Agrepedia, and TagThisCar. Paco received his BS Math Sci and MS Comp Sci degrees from Stanford University, and has 25+ years technology industry experience ranging from Bell Labs to early-stage start-ups.
Paco is a frequent speaker at data conferences. In the last year, his speaking dates include Strata, OSCON, Big Data Tech Con, and Data Day Texas.
Paco is author of the O'Reilly book: Enterprise Data Workflows with Cascading.
Paco's Wikipedia Page
Paco on Twitter, Linkedin, Slideshare, Github
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