What is PCA? PCA is one of the most used unsupervised algorithms and dimensionality reduction algorithms. PCA reduces the dimensions of a D-dimensional data set by projecting onto a K-dimensional ...
Dimensionality Reduction
What is Dimensionality Reduction? In classification, we worked with datasets composed of only two independent variables. We did so for two reasons: Because we needed two dimensions to bet...
Thompson Sampling
What is Thompson Sampling? Unlike UCB, which selects a high confidence interval, Thompson sampling creates a probabilistic perception. Algorithm of Thompson Sampling Let’s assume that we are p...
Upper Confidence Bound (UCB)
What is UCB? UCB finds a way to combine “exploration” with “exploitation” to get the best result. Algorithm of UCB Let’s assume that we are planning an advertisement. There are three steps t...
Reinforcement Learning
What is Reinforcement Learning? Reinforcement Learning is a powerful branch of Machine Learning. It is used to solve interacting problems where the data observed up to time $t$ is considered to dec...
Eclat
What is Eclat? The Eclat model serves to review all combinations and tells you which ones to focus on. The Eclat algorithm has only one part unlike Apriori: Support It’s very similar to Bayes...
Apriori
What is Apriori? Apriori is an algorithm for frequent itemset mining and association rule learning. It proceeds by identifying the frequent individual items in the dataset and extending them to lar...
Association Rule Learning
What is Association Rule Learning? Association rule learning is a rule-based machine learning method for discovered in the dataset using some measures of interestingness. It is intended to identif...
Hierarchical Clustering
What is Hierarchical Clustering? Hierarchical Clustering gives results very similar to K-means clustering, But the whole process is different. There are two types of hierarchical clustering: ...
K-Means Clustering
What is K-Means Clustering? K-Means Clustering is performed by updating the centroid based on the distance between the centroid and each data point. Steps Step 1 Choose the number $K$ of c...