The Power of Dimensionality Reduction
A comprehensive guide to spectral clustering and its role in dimensionality reduction, enhancing data analysis, and uncovering patterns in machine learning.
Recent posts
Mysteries of Clustering
Discover the inner workings of clustering algorithms, from K-Means to Spectral Clustering, and how they unveil patterns in machine learning, bioinformatics, and data analysis.
Convergence of Topology and Data Science
Dive into Topological Data Analysis (TDA) and discover how its methods, such as persistent homology and the mapper algorithm, help uncover hidden insights in high-dimensional and complex datasets.
Understanding Customer Lifetime Value
Discover the importance of Customer Lifetime Value (CLV) in shaping business strategies, improving customer retention, and enhancing marketing efforts for sustainable growth.
Mastering Bayesian Statistics: An In-Depth Guide to MCMC
Discover how Bayesian inference and MCMC algorithms like Metropolis-Hastings can solve complex probability problems through real-world examples and Python implementation.
Demystifying MCMC: A Practical Guide to Bayesian Inference
Explore Markov Chain Monte Carlo (MCMC) methods, specifically the Metropolis algorithm, and learn how to perform Bayesian inference through Python code.
A Closer Look at the Classic Bell Curve
Discover the significance of the Normal Distribution, also known as the Bell Curve, in statistics and its widespread application in real-world scenarios.
Marina Viazovska: Fields Medalist and Pioneer in Sphere Packing
Marina Viazovska won the Fields Medal in 2022 for her remarkable solution to the sphere packing problem in 8 dimensions and her contributions to Fourier analysis and modular forms.
Text Preprocessing Techniques for NLP in Data Science
Text preprocessing is a crucial step in NLP for transforming raw text into a structured format. Learn key techniques like tokenization, stemming, lemmatization, and text normalization for successful NLP tasks.
Mathematics of Machine Learning: A Comprehensive Exploration
This article delves into the core mathematical principles behind machine learning, including classification and regression settings, loss functions, risk minimization, decision trees, and more.