Source: https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ Introduction Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important amongst all forms of regression analysis.… Continue reading 7 Types of Regression Techniques you should know!
Before starting to learn how to use Hadoop ecosystem, it is important to understand why we need Hadoop ecosystem in addition to traditional relational database systems. So, let’s understand what the limitations of RDBMS are. Understanding RDBMS (relational database management systems) limits First, let’s see the landscape of databases existing today such as SQL… Continue reading Why move away from Relational Databases?
The dawn of the Trump era holds uncertainty for the power sector, but the trends pointing toward a cleaner grid are still very much in play AUTHOR Gavin Bade@GavinBade PUBLISHED Jan. 23, 2017 If there’s one hallmark of the power sector at the beginning of 2017, it’s uncertainty. At the time of our last trend… Continue reading 10 trends shaping the electric utility industry in 2017
Key syntaxes Creating a table CREATE TABLE Users (name TEXT, age INTEGER, email TEXT) Inserting an instance INSERT INTO Users VALUES (‘Charles’, 20, ‘email@example.com’) Deleting an instance DELETE FROM Users WHERE email=’firstname.lastname@example.org’ Selecting an instance SELECT * FROM users WHERE email=’email@example.com’ ORDER BY name Updating an instance UPDATE Users SET name=’mike’ WHERE email = ‘firstname.lastname@example.org’
Difference between a mixture model and HHM If we examine a single time slice of the model, it can be seen as a mixture distribution with component densities given by It can be interpreted as an extension of a mixture model where the choice of mixture component for each observation is not independent but depends… Continue reading Hidden Markov model
Markov Models A way to exploit special sequential aspect (e.g. correlations between observations that are close in the sequence). For example, rainy day or not. If we treat the data as i.i.d., then the only information we glean from the data is the frequency of rainy days without any weather trends that last few days.… Continue reading What is a Markov model?
What is sequential data? Data with poor or no i.i.d assumption Often found in time series data. For instance, Rainfall measurements on successive days at a particular location Daily values of a currency exchange rate Acoustic features at successive time frames used for speech recognition) Stationary vs. nonstationary sequential distributions Stationary: data evolves in time… Continue reading Sequential data