January 30, 2017
Introduction Deep Learning isn't a recent discovery. The seeds were sown back in the 1950s when the first artificial neural network was created. Since then, progress has been rapid, with the structure of the neuron being "re-invented" artificially. Computers and mobiles have now become powerful enough to identify objects from images. Not just images, they can […]
January 19, 2017
Introduction Clustering algorithms are a part of unsupervised machine learning algorithms. Why unsupervised ? Because, the target variable is not present. The model is trained based on given input variables which attempt to discover intrinsic groups (or clusters). As the target variable is not present, we can't label those groups. Then, how is it done? That's the interesting part […]
January 12, 2017
Introduction The best way to learn a new skill is by doing it! This article is meant to help R users enhance their set of skills and learn Python for data science (from scratch). After all, R and Python are the most important programming languages a data scientist must know. Python is a supremely powerful and a […]
January 5, 2017
Introduction Recruiters in the analytics/data science industry expect you to know at least two algorithms: Linear Regression and Logistic Regression. I believe you should have in-depth understanding of these algorithms. Let me tell you why. Due to their ease of interpretation, consultancy firms use these algorithms extensively. Startups are also catching up fast. As a result, in an […]
December 28, 2016
Introduction Many people are pursuing data science as a career (to become a data scientist) choice these days. With the recent data deluge, companies are voraciously headhunting people who can handle, understand, analyze, and model data. Be it college graduates or experienced professionals, everyone is busy searching for the best courses or training material to become […]
December 20, 2016
Introduction Last week, we learned about Random Forest Algorithm. Now we know it helps us reduce a model's variance by building models on resampled data and thereby increases its generalization capability. Good! Now, you might be wondering, what to do next for increasing a model's prediction accuracy ? After all, an ideal model is one […]
December 14, 2016
Introduction Treat "forests" well. Not for the sake of nature, but for solving problems too! Random Forest is one of the most versatile machine learning algorithms available today. With its built-in ensembling capacity, the task of building a decent generalized model (on any dataset) gets much easier. However, I've seen people using random forest as a […]
December 6, 2016
Introduction "The road to machine learning starts with Regression. Are you ready?" If you are aspiring to become a data scientist, regression is the first algorithm you need to learn master. Not just to clear job interviews, but to solve real world problems. Till today, a lot of consultancy firms continue to use regression techniques […]