Markov chain Monte Carlo is a powerful technique that combines the concepts of Markov chain and Monte Carlo methods. It provides a way to sample from otherwise intractable probability distributions. MCMC methods are statistical techniques that provide a way to sample from complex, high-dimensional probability distributions that are otherwise intractable or difficult to analyze mathematically. MC..

Unsupervised or Supervised Classification This post will focus on classification algorithms, such as K-means, model-based clustering, K nearest neighbours and probabilistic classifiers. K-means K-means clustering is a popular unsupervised machine learning algorithm to partition a dataset into k distinct clusters. It aims to minimize the within-cluster sum of squares by assigning data points to c..

Principal Components Analysis (PCA) is a popular technique for analysing high-dimensional data. PCA is a mathematical procedure that transforms a set of correlated variables into a new set of uncorrelated variables, called "Principal Components" while retaining most of the variability of the original data. To perform PCA on a dataset of N, p-dimensional data point x_j ∈ Rᴾ, we need to follow the..

Exploratory Data Analysis(EDA) is always a starting point for Data analysis, and it is about getting an intuitive understanding of the data. First, you need to decide what to test, and then you can use statistics to test out what the data delivers. Data Quality: Where does the data come from, and how accurate are they? The below table summarises the star rating of data quality suggested by David..

What's the difference? - BIgn Data: massive, quickly built, vary, perform with relational DB - Data Mining: the process of automatic searching and analyzing data, discovering patterns, including preprocessing, - Machine Learning: a subset of AI, analyze data and make intelligent decisions, trained with large sets of data, learn from examples, allow the machines to solve problems on their own and..