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While our text analytics session takes on the world of unstructured data, there’s another form of unstructured data that often goes unnoticed. Audio, photo and video data are notoriously difficult to analyze with traditional statistical methods.
For instance, if you wanted to build face recognition software, how do you train your computer to find a face? How does a computer read human handwriting? How do you build a car that drives itself?
For these problems and many more, machine learning is becoming increasingly the default solution. Very few people on the planet take on such high levels of expertise to be able to create machine learning algorithms and are employed by the biggest companies you hear of.
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You will learn:
- The learning problem
- Landscape of application and problem areas
- Learning algorithms: random forest, support vector machines, gradient boosting models amongst other.
- Process workflow: data collection, cleaning, processing and manipulation
- In class project: Build an algorithm than can identify human handwriting with error rates of 1 in 1000.
Part time/ Full time: Part Time
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