Machine Learning Course

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. 
LinkedIn, Google, Yahoo, Twitter and many other big brands hire top notch talent for their machine learning skills. A fair warning though, you will be challenged intensely in the 8 hour program. Take this course for one of two reasons:

• “I want to know what the whole fuss is about, but I don’t necessarily plan to actually do it myself.”

• I’m keen on pushing past the standard mass market skill set of most analysts in today’s industry

In the session, we will be exploring topics mentioned here and other lesser known concepts.

• The learning problem

• Landscape of application and problem areas

• Learning algorithms: random forest, support vector machines, gradient boosting models amongst other.

• Process workflow: data col lection, cleaning, processing and manipulation

• In class project: Build an algorithm than can identify human handwriting with error rates of 1 in 1000.