The Apriori Algorithm looks at millions of transactions to assist shops figure out which items are typically bought together. Students and anyone who want to be data scientists need to learn this in order to grasp how recommendation engines work, like the ones on Amazon and Netflix. This article breaks down the intricate logic into …
Most people who want to learn about machine learning start with K-Means. While K-Means is simple, it often fails when faced with datasets that aren’t perfectly circular or contain random outliers. This is where DBSCAN clustering in ML becomes a game-changer. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) solves this by examining how “crowded” …
DBSCAN Clustering in ML – Density Based Clustering Read More »
One of the most important things for any data scientist to do is figure out how data points naturally clump together. A lot of beginners start with K-Means, but they have a hard time with its major flaw: they have to guess how many clusters there are before they can use it. Hierarchical Clustering in …
Data is the fuel for modern AI, but sometimes, having too much of it can be a problem. This is what machine learning models face when they deal with “high-dimensional data”. Every extra feature (column) adds a layer of complexity that can lead to slow processing and poor accuracy. Understanding an introduction to dimensionality reduction …
Naive Bayes classifiers are a group of machine learning models that use Bayes’ Theorem to work well. They are meant to help with classification problems by figuring out how likely it is that a data point belongs to a certain group. The “naive” part comes from the idea that all input features are separate, which …
The K-nearest neighbor(knn) algorithm is a learning method that classifies data based on the “proximity” principle. By utilising the k-nearest neighbor algorithm formula, specifically Euclidean distance, the KNN model identifies the most similar data points to make predictions. This k-nearest neighbor algorithm working process involves a majority vote for k-nearest neighbor algorithm classification tasks. In …
One decision tree in machine learning is easy to follow, but it can be biased or too sensitive to certain data points. In random forest algorithm in machine learning is like a “committee of experts” because it has many trees that vote on an outcome and the one with the most votes wins. When students …
You might be surprised at how machines really “think” about a problem. This is where the idea of a decision tree in machine learning model comes in. It is one of the most user-friendly AI tools because it turns complicated datasets into easy-to-read options. If you want to learn how to use predictive analytics, the …
Logistic regression is one of the most reliable and commonly used tools in the large toolbox of supervised machine learning. Many people think it has to do with predicting numerical trends because of its name, but it is actually a key part of logistic regression in machine learning classification. This algorithm doesn’t try to guess …
The machine learning field has grown a lot in the last ten years, and most of the research has been on supervised machine learning. This is the most popular branch of AI because it learns from feedback, just like people do. People can sometimes learn by instinct, but computers need a structured supervised machine learning …
