This blog post is the continuation of my previous articles part 1, part 2 and part 3. Caution: The Difference Between Training Metrics and Evaluation Metrics Sometimes, the model training procedure uses a different metric (also known as a loss function) than the evaluation. This can happen in the instance when we are re-appropriating a model for a different task than it was designed for. For example, we might train a personalized recommender by minimizing the loss between its predictions and observed ratings, and then use this recommender to produce a ranked list of recommendations. This is not an optimal scenario. It makes the life of the model difficult by asking it to do a task that it was not trained to do.

Big data and analytics have become crucial to business. But will that spine develop, or will it change the landscape of business yet again? Here's a sneak peek into what the following months look like. Just a while ago big data was a lucrative new phenomenon promising a smooth business takeover. Now, since data and analytics are imperative to business and deeply embedded, the question arises whether technology will have a growth spurt in the coming year, continue to mold and restructure businesses or be replaced by something else.

Since about 2010, "Big Data" has become the ubiquitous term to describe all the data that is generated by people from their smartphones, web browsing history, social media and purchasing behaviour, together with any other information that organizations hold about them. Why is big data different to any other type of data? However, the term "Big Data" tends to be applied to large collections of different types of data which are often volatile and changeable, and where one would struggle to analyse it using traditional computer hardware and software. It's also the case that big data often incorporates certain types of data that were not widely used for customer analysis until relatively recently. What people write and say can be analysed to identify what they are talking about sentiments being expressed.

Many people have this doubt, what's the difference between statistics and machine learning? Is there something like machine learning vs. statistics? From a traditional data analytics standpoint, the answer to the above question is simple. Machine learning is all about predictions, supervised learning, unsupervised learning, etc. Statistics is about sample, population, hypothesis, etc. Well, let's see if they are actually that different! They are both concerned with the same question: how do we learn from data?

Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we'll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM]. All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which one to choose for their particular problem. Classical and the most correct but least satisfying response to that question is "it depends!".

R is probably every data scientist's preferred programming language (besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets. There are so many libraries, applications and techniques exist to explore data in R that I'm sure even experts don't know them all! Aspiring data scientists who are reading this though, fear not, for you are well on your way to understanding these secrets. The links provide the ability to download the pdfs of the books. Authored by: Trevor Hastie and Rob Tibshirani, recognized Stanford professors and authors of "The Elements of Statistical Learning" What you'll learn: Implementation of statistical and machine learning techniques in R This book will teach you what you need to know, without harassing you much about the math behind it all.

Many people have this doubt, what's the difference between statistics and machine learning? Is there something like machine learning vs. statistics? From a traditional data analytics standpoint, the answer to the above question is simple. Machine learning is all about predictions, supervised learning, unsupervised learning, etc. Statistics is about sample, population, hypothesis, etc. Well, let's see if they are actually that different!