You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results.
Researchers Are Making More than $1 Million at a Nonprofit" declares the NYT . It's certainly not the first article where commentators have opined upon how much top AI researchers make and how it reflects the rosy economics of young people going into a career in machine learning or data science. While the 800k that the AI superstar pulled in is nothing to scoff at, especially for the average thirty-something year-old, hundreds of relatively unknown quants in finance are paid this much every year. In his heyday, a big name like Emanuel Derman would have been paid much more than 800k (perhaps an order of magnitude more). So, rather than be a cause for bullishness, the numbers suggest to me that AI researchers and data scientists are underpaid relative to their quant brethren.