Emoji is becoming a global language understandable by anyone who expresses... emotion. With the pervasiveness of these little Unicode blocks, we can perform analytics on their use throughout social media to gain insight into sentiments around the world.
Let’s take a look on what R users are saying about their salaries. Note that the following results could be biased because of unrepresentative and in some cases small samples.
The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.)
New KDnuggets poll asks 1) What Data Science/Machine Learning-related skills you currently have, and 2) Which skills you want to add or improve? If you are human, please vote and we will analyze and publish the results.
Using the ATTOM dataset, we extracted data on sales transactions in the USA, loans, and estimated values of property. We developed an optimal prediction model from correlations in the time and status of ownership as well as the time of the year of sales fluctuations.
Through an analysis of 1.5M papers from arXiv, this study reviews the evolution of gender diversity across disciplines, countries, and institutions as well as the semantic differences between AI papers with and without female co-authors.
What’s the best way to execute your data integration tasks: writing manual code or using ETL tool? Find out the approach that best fits your organization’s needs and the factors that influence it.
As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.
Predictor collinearity (also known as multicollinearity) can be problematic for your regression models. Check out these rules of thumb about when, and when not, to be concerned.
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.
Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.
This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside.
By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.
Types of Bias in Machine Learning
New Poll: Data Science Skills
How to Become More Marketable as a Data Scientist
12 NLP Researchers, Practitioners & Innovators You Should Be Following
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