ml-diaries-by-fahd.hashnode.devReinforcement Learning: The Big PictureIn the previous posts of this series, we explored Supervised Learning, where models learn from labeled data, and unsupervised learning, where patterns are discovered without labels. But what if there are no labels, no predefined groups, and no clear ...Jan 19·4 min read
ml-diaries-by-fahd.hashnode.devApriori Algorithm — Learning Association Rules the Simple WayAs part of our unsupervised learning series today, we will talk about a famous algorithm of an association type. While exploring unsupervised learning, I came across the Apriori Algorithm. At first, it sounded complex, but once I broke it down with r...Jan 16·4 min read
ml-diaries-by-fahd.hashnode.devPCA Explained Simply: Understanding Dimensionality ReductionIn our previous article on unsupervised learning, we covered a clustering algorithm. Today we will be covering a common algorithm that comes under the category of dimensionality reduction. So, in machine learning, having more data is usually helpful ...Jan 12·6 min read
ml-diaries-by-fahd.hashnode.devDBSCAN Explained Simply: Clustering with Noise and Arbitrary ShapesIn this article of our Unsupervised Learning series, we explore another important clustering algorithm that comes right after K-Means in popularity and usefulness — DBSCAN. While K-Means works very well for many problems, it assumes that clusters are...Jan 11·5 min read
ml-diaries-by-fahd.hashnode.devSimplifying K-Means Clustering: A Beginner's Guide to Unsupervised LearningThis is another article in our Unsupervised Learning series. Today, we’ll explore one of the most popular and intuitive algorithms used in unsupervised learning — K-Means Clustering. Before jumping into K-Means, let’s quickly recall what unsupervised...Jan 9·4 min read
