When I hear that a system has a one in a trillion (1,000,000,000,000) chance of failure, I immediately translate that…
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vikashraj luhaniwal
Recommending news articles based on already read articlesContent based recommendation in Python from…Why Probability distribution is must in DS/ML —As the name suggests…
Continue ReadingA Gentle Introduction to Probability Metrics for Imbalanced Classification
Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of…
Continue ReadingRandom sample overlap
To make the problem slightly more general, take two samples of size √n from a population of size n where…
Continue ReadingDevelop an Intuition for Bayes Theorem With Worked Examples
Last Updated on December 9, 2019Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively…
Continue ReadingHow to Use an Empirical Distribution Function in Python
An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not…
Continue ReadingA Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically,…
Continue ReadingA Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There…
Continue ReadingA Gentle Introduction to Cross-Entropy for Machine Learning
Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…
Continue ReadingHow to Calculate the Divergence Between Probability Distributions
Last Updated on October 18, 2019 It is often desirable to quantify the difference between probability distributions for a given…
Continue ReadingHow to Develop a Naive Bayes Classifier from Scratch in Python
Last Updated on October 7, 2019 Classification is a predictive modeling problem that involves assigning a label to a given…
Continue ReadingA Gentle Introduction to Bayes Theorem for Machine Learning
Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can…
Continue ReadingProbability for Machine Learning (7-Day Mini-Course)
This is called the “Boy or Girl Problem” and is one of many common toy problems for practicing probability. Post…
Continue ReadingHow to Develop an Intuition for Probability With Worked Examples
Probability calculations are frustratingly unintuitive. Our brains are too eager to take shortcuts and get the wrong answer, instead of…
Continue ReadingHow to Develop an Intuition for Joint, Marginal, and Conditional Probability
Probability for a single random variable is straight forward, although it can become complicated when considering two or more variables.…
Continue ReadingA Gentle Introduction to Joint, Marginal, and Conditional Probability
Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the…
Continue ReadingA Gentle Introduction to Probability Density Estimation
Probability density is the relationship between observations and their probability. Some outcomes of a random variable will have low probability…
Continue ReadingContinuous Probability Distributions for Machine Learning
The probability for a continuous random variable can be summarized with a continuous probability distribution. Continuous probability distributions are encountered…
Continue ReadingDiscrete Probability Distributions for Machine Learning
The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used…
Continue ReadingA Gentle Introduction to Probability Distributions
Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all…
Continue ReadingWhat Is Probability?
Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty…
Continue Reading5 Reasons to Learn Probability for Machine Learning
Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning,…
Continue ReadingResources for Getting Started With Probability in Machine Learning
Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and…
Continue ReadingThe information paradox
The information paradoxAndrea BerdondiniBlockedUnblockFollowFollowingJul 9ABSTRACT: The following paradox is based on the consideration that the value of a statistical datum…
Continue ReadingWHAT and WHY of Log Odds
WHAT and WHY of Log OddsPiyush AgarwalBlockedUnblockFollowFollowingJul 8The three main categories of Data Science are Statistics, Machine Learning and Software Engineering.…
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