Machine Learning (ML) Summary of Regression Techniques Regression is a technique based on statistics to model the relationship between a set of variables to make predictions on unseen data. We explored are Linear, Logistic, Polynomial, Ridge, Lasso, Elastic Net, Stepwise regression.
Machine Learning (ML) Bayesian model A Bayesian model is a statistical model where we use probability to represent both the uncertainty regarding the output and input to the model. The basic idea is that we start by assuming something which is adjusted based upon input data. We look into Bayesian Linear Regression as well
Machine Learning (ML) Hidden Markov Model Hidden Markov Model is a stochastic model describing a sequence of possible events in which the probability of each event depends on the state attained in the previous event. Markov model can be used in real life forecasting problems. Simple Markov model cannot be used for customer level predictions
Machine Learning (ML) Markov Chain Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It refers to the sequence of random variables such a process moves through, with the Markov property of serial dependence
data science Basic Data Science concepts everyone needs to know In this article, we explored some of the basic data science concepts everyone needs to understand to ace things in real life. We demonstrated basic statistic measurements such as median, bayesian statistics, probability distribution, dimensionality reduction and over and under sampling of data.