Machine Learning Expedition

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Machine Learning Expedition

How PCA Makes Sense of Your Multitude of Measurements

Principal Component Analysis (PCA) is a powerful statistical technique used for dimensionality reduction in data analysis and machine learning. Its primary objective is to transform a set of possibly correlated variables into a set of linearly uncorrelated variables called principal components. PCA is widely used in fields such as exploratory

Understanding the Difference: Linear Regression vs. Linear Mixed-Effects Models

Understanding the Difference: Linear Regression vs. Linear Mixed-Effects Models

Linear regression and linear mixed-effects models are two statistical methods often used in data analysis, but they serve different purposes and have distinct features. Linear Regression: It's a basic form of statistical analysis, ideal for examining the relationship between two variables. It assumes independence of observations and homogeneity

An Introduction to Proximal Policy Optimization (PPO) in Reinforcement Learning

Proximal Policy Optimization (PPO) is an advanced reinforcement learning algorithm that has become very popular in recent years. In this comprehensive guide, we will cover: * What is PPO and how it relate to reinforcement learning * The key components and techniques used in PPO * Actor-critic method * Clipping the objective function * Adaptive

Top Machine Learning Algorithms for Real-World Machine Learning

Top Machine Learning Algorithms for Real-World Machine Learning

Linear Regression Linear regression is one of the most fundamental and commonly used statistical and machine-learning techniques. At its core, linear regression involves fitting a straight line through a set of data points to be able to make predictions about future data points. The basic idea behind linear regression is

Introduction to Word2Vec

Word2vec is a popular group of related models used to produce word embeddings. Word embeddings are vector representations of words that capture semantic meaning allowing words with similar meanings to have similar representations. Word2vec models are shallow, two-layer neural networks trained on large amounts of text data to reconstruct linguistic

Finding the Needles in the Haystack: A New Approach to Anomaly Detection in Tabular Data

Anomaly detection is like searching for a needle in a haystack. It's about identifying unusual data points that stand out from the crowd, often indicating errors, fraud, or unexpected events. But what if you have limited data, or need to understand why something is anomalous? That's

Understanding RuleFit: The Algorithm That Combines Decision Trees and Linear Models

The RuleFit algorithm stands out as a unique approach in the realm of machine learning. It ingeniously combines the interpretability of decision trees with the simplicity and effectiveness of linear models. In this blog, we'll dive deep into what RuleFit is, how it works, and its practical applications.