In a data science project, almost always the most time consuming and messy part is the data gathering and cleaning. Everyone likes to build a cool deep neural network (or XGboost) model or two and show off one's skills with cool 3D interactive plots. But the models need raw data to start with and they don't come easy and clean. But why gather data or build model anyway? The fundamental motivation is to answer a business or scientific or social question.
In a data science project, almost always the most time consuming and messy part is the data gathering and cleaning. Everyone likes to build a cool deep neural network (or XGboost) model or two and show off one's skills with cool 3D interactive plots. But the models need raw data to start with and they don't come easy and clean.
"I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful." "By taking this course my perception has changed and now data science for me is more about data wrangling. Welcome to The Complete Course for Machine Learning Engineers.
If IBM is looking for a new application for its Watson machine learning tools, it might consider putting health care providers' procurement and systems integration woes ahead of curing cancer. The fall-out from that project has now prompted the resignation of the cancer center's president, Ronald DePinho, the Wall Street Journal reported Thursday. The university recently published an internal audit report into the procurement processes that led it to hand almost $40 million to IBM and over $21 million to PwC for work on the project, almost all of it without board approval. It noted that the scope of its review was limited to contracting and procurement practices and compliance issues, and did not cover project management and system development activities. The audit "should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state," because a separate review of those aspects of the project is being conducted by an external consultant, it said.
Thompson sampling, a Bayesian method for balancing exploration and exploitation in bandit problems, has theoretical guarantees and exhibits strong empirical performance in many domains. Traditional Thompson sampling, however, assumes perfect compliance, where an agent's chosen action is treated as the implemented action. This article introduces a stochastic noncompliance model that relaxes this assumption. We prove that any noncompliance in a 2-armed Bernoulli bandit increases existing regret bounds. With our noncompliance model, we derive Thompson sampling variants that explicitly handle both observed and latent noncompliance. With extensive empirical analysis, we demonstrate that our algorithms either match or outperform traditional Thompson sampling in both compliant and noncompliant environments.