Evaluation metrics are co-related to machine learning tasks. The tasks of classification, regression, ranking, clustering, topic modeling, etc, all have different metrics. Some metrics, such as precision recall, are of use for multiple tasks. Classification, regression, and ranking are examples of supervised learning, which comprises a majority of machine learning applications. In this series, we’ll […]
This is a continuation of my previous article. The elements of a data product do not have to be built in a set order. The professional approach is to build by giving the highest technical risk preference. Start with the element that is riskiest first and go from there. An element can be technically risky […]
Data scientists need software engineering skills—just not all of them. By “professional” data science programmers, I mean data scientists with essential data product engineering skills. Professionalism isn’t something you can own like a certification or hours of experience; I’m talking about professionalism as an approach. The professional data science programmers have general strategies for recognizing […]
Though the word ‘Metadata’ is quite familiar to all of us, many of us are not very clear as to what exactly is metadata or why is it as important as data. Let us try to figure this out by plunging headlong! Some common concepts about Metadata are: Metadata is data that provides information about other […]
An offshoot of machine learning, Deep Learning adopts various approaches to tackling the primary and most important goal of AI research: getting computers to model our world to the extent that they become capable to acquire something akin to what we humans call intelligence. All deep learning approaches share a very basic trait on a conceptual level. […]
The data your business generates on a daily basis holds immense potential and big data analytics can help reveal useful insights from it. But before you make the decision to start a big data project, it is better to ask yourself these questions: 1) Is there an agenda behind the project? Yes, there should be […]
Interacting with a customer care helpline or a call centre is usually a nightmarish experience for most of us. People would often prefer to live with the problem rather than try to elicit support from some customer helplines, and are therefore held in contempt by many. Recent advances in data collection, data analysis, and artificial […]
The Quant Crunch report indicates that the demand for a data scientist is expected to rise 28% by 2020 and this does not encourage the fact that data science jobs are as such very difficult to fill now. Undoubtedly, academic institutions are actively engaged with fulfilling this rapidly increasing need, but the demand for data […]
When someone mentions Hadoop, they generally don’t refer to the core Apache Hadoop project, but instead are referring to its technology along with an ecosystem of other projects that work with Hadoop. It’s just like when someone tells you they are using Linux as their operating system but they mean the thousands of applications that […]
How do we squeeze value out of Data Science? It is well established that Data science plays a key role in almost all specialized fields. The outcome of an election could be swayed from losing to winning it by proper classification of potential voters. The ability to predict user engagement accrues large gains in advertising. […]
