Statistics is the Grammar of Data Science — Part 5/5Statistics refresher to kick start your Data Science journeySemi KoenBlockedUnblockFollowFollowingFeb 16This is the 5th (and…

Continue Reading# probability

## Solving for probability given entropy

If a coin comes up heads with probability p and tails with probability 1-p, the entropy in the coin flip…

Continue Reading## How to Calibrate Undersampled Model Scores

How to Calibrate Undersampled Model ScoresImbalanced data problems in binary prediction models and a simple but effective way to take care…

Continue Reading## Can you Solve TED’s Frog Riddle? Can TED?

Critics argue that it’s not. For those of you who want to see the problem laid out in detail, you…

Continue Reading## Probability — Fundamentals of Machine Learning (Part 1)

By plugging this into the chain rule, we find that in this scenario we get P(x, y) = P(x|y) ⋅…

Continue Reading## Learning NLP Language Models with Real Data

There are far to many possible sentences in this method that would need to be calculated and we would like…

Continue Reading## Statistics is the Grammar of Data Science — Part 2

To visualise the probability, we plot the dataset as a curve. The area under the curve between two points corresponds…

Continue Reading## Are you mixing up odds with probability?

And in high school they tend to teach us about probabilities, not odds. A probability is defined as the number…

Continue Reading## Markov Chain Monte Carlo in Python

Markov Chain Monte Carlo in PythonWill KoehrsenBlockedUnblockFollowFollowingFeb 9, 2018A Complete Real-World ImplementationThe past few months, I encountered one term again and…

Continue Reading## Marketing Analytics through Markov Chain

Marketing Analytics through Markov ChainRidhima KumarBlockedUnblockFollowFollowingJan 6Image Source : http://setosa. io/ev/markov-chains/Imagine you are a company selling a fast-moving consumer good in the…

Continue Reading## Hyper-parameter Optimization

Hyper-parameter OptimizationJon-Cody SokollBlockedUnblockFollowFollowingJan 3Photo by Paul Green on UnsplashIf you were to count all the possible classification algorithms and their parameters…

Continue Reading## Probability theory and the optimal dating strategy for 2018

It’s 1/N. And as n gets larger the larger timeframe we consider, this probability will tend to zero. Alright, you…

Continue Reading## Dice, Polls & Dirichlet Multinomials

Even with increasingly better computational tools, such as MCMC, models based on conjugate distributions are advantageous.Beta-BinomialOne of the better known…

Continue Reading## Unfolding Naive Bayes from Scratch: Part 2

When doing the calculations of probability of the given test sentence in the above section, we did nothing but implement…

Continue Reading## Probability Part 2: Conditional Probability

By thinking of conditioning as a restriction on the size of the event space, we can measure the conditional probability…

Continue Reading## Bayes’ Theorem: The Holy Grail of Data Science

1 Statistical resultsThe figure tells us that we have picked…… 148 times a blueberry from the bowl X: n(s=X, y=B)=148……

Continue Reading## Monty Hall’s paradox — solve it by simulation!

D in our case is when the host choosing door B and there is no price behind it.Let’s create a…

Continue Reading## Journey to Understand Bayes’ Theorem Visually

There is also a possibility for another event B to occur after A and the odds of that are denoted…

Continue Reading## Using Markov Chain Monte Carlo method for project estimation

In particular, we are interested in finding the number of story points we can complete in one iteration with 95%…

Continue Reading## Naive Bayes classification from Scratch in Python

All together posterior probability in terms of the joint probability distribution (neglecting denominator P(x)) is written as:Now to calculate each…

Continue Reading## Bayesian Convolutional Neural Networks with Bayes by Backprop

This results in the subsequent equation for convolutional layer activations b:where ϵj ∼ N(0, 1), Ai is the receptive field,…

Continue Reading## The naive Bayes classifier

In this case, what is the probability Y belongs to a distinct class k given an observation x?πₖ is the…

Continue Reading## Probability and Statistics explained in the context of deep learning

Probability and Statistics explained in the context of deep learningPhoto by Josh Appel on UnsplashThis article is intended for beginners in deep…

Continue Reading## Receiver Operating Characteristic Curves Demystified (in Python)

The model performance is determined by looking at the area under the ROC curve (or AUC)..To create this, probability distribution,…

Continue Reading## Probability & Statistics for Data Science (Series)

I would like to mention that my focus in these posts would be to give intuition on every topic and…

Continue Reading