Singular Value Decomposition Algorithms Singular Value Decomposition concepts Singular Value Decomposition (SVD) is a matrix factorization technique used in various machine learning and data analysis applications. It decomposes a matrix into three separate matrices that capture the underlying structure of the original matrix. The three matrices that SVD produces are:   U: a unitary matrix that represents the left singular vectors of the original matrix. S: a diagonal matrix that represents the singular values of the original matrix. V: a unitary matrix that represents the right singular vectors of the original matrix. Here is an example of how SVD works : Suppose we have a matrix that represents the ratings of users for different movies. We can use SVD to decompose this matrix into three separate matrices: one matrix that represents the preferences of users, one matrix that represents the importance of each movie, and one matrix that captures the relationship between users and m

Apriori algorithm with Python Code Apriori algorithm Concepts classic association rule learning algorithm used in data mining and machine learning to identify relationships between different items in a dataset. It is used to extract frequent item sets and association rules from large datasets, by exploring the relationships between different items based on their frequency of occurrence. Here is an example of how the Apriori algorithm works : Suppose we have a dataset of customer transactions, where each transaction includes a list of items that the customer has purchased. We want to identify relationships between different items and use this information for targeted marketing campaigns. We use the Apriori algorithm to extract frequent item sets and association rules from the dataset. The algorithm works by first identifying all the frequent single items, then using these to generate frequent pairs, and so on until a stopping criterion is met. Apriori algorithm  Algorithm Define the pro

Collaborative filtering Algorithms Collaborative Filtering Concept Collaborative filtering is a technique used in recommendation systems to predict a user's preferences based on their past behaviour and the behaviour of similar users. This method is predicated on the idea that people who have had similar preferences in the past would continue to have those preferences.  Here is an example of how collaborative filtering works : Suppose we have a dataset of user ratings for different movies. We want to recommend movies to users based on their past ratings. We use collaborative filtering to find similar users and recommend movies that these users have rated highly. Collaborative filtering can be done using two approaches : user-based and item-based. In the user-based approach, the algorithm finds users who have similar ratings and recommends items that these users have rated highly. In the item-based approach, the algorithm finds items that are similar to the ones the user has rated

K-Means Clustering of U nsupervised Learning Algorithm Concepts of  K-Means clustering K-Means clustering is a popular unsupervised machine learning algorithm used to cluster or group similar data points together in a dataset. The algorithm works by partitioning the data into K clusters, where K is a predetermined number chosen by the user. The goal is to minimize the distance between the data points within each cluster while maximizing the distance between the clusters. Here is an example of how K-Means clustering works : Suppose we have a dataset of customer transactions, where each transaction includes the customer's age, income, and spending behaviour. We want to group customers with similar spending behaviour together for targeted marketing campaigns. We use K-Means clustering to partition the data into K clusters based on the customer's spending behaviour. The algorithm assigns each customer to a cluster based on the similarity of their spending behaviour. K-Means clust

Unsupervised Learning Algorithm -  Hierarchical Clustering Hierarchical clustering Concepts Hierarchical clustering is a popular unsupervised machine learning algorithm used to cluster or group similar data points together in a dataset. Hierarchical clustering does not need the user to predetermine the number of clusters, in contrast to K-Means clustering. The algorithm works by creating a hierarchy of clusters, where each data point initially forms its own cluster, and clusters are successively merged based on their similarity. Here is an example of how Hierarchical clustering works : Suppose we have a dataset of customer transactions, where each transaction includes the customer's age, income, and spending behaviour. We want to group customers with similar spending behaviour together for targeted marketing campaigns. We use Hierarchical clustering to create a hierarchy of clusters based on the similarity of their spending behaviour. The algorithm initially assigns each customer

Principal Component Analysis Algorithm Principal Component Analysis Concept Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional datasets into a lower-dimensional space while preserving as much of the variance as possible. It works by identifying the principal components that capture the most significant variation in the data and projecting the data onto these components. Suppose we have a dataset of images with 1000 pixels each. We want to reduce the dimensionality of the dataset to 100 pixels to reduce the computational complexity. We use PCA to identify the principal components that capture the most significant variation in the images. We project the images onto these components to obtain a lower-dimensional representation of the images. Principal Component Analysis  Algorithm Define the problem and collect data. Compute the covariance matrix of the data. Compute the eigenvectors and eigenvalues of the covariance matrix. Choos

Support Vector Machine Learning Algorithm Concepts of Support Vector Machines The supervised learning technique Support Vector Machines (SVM) can be applied to both classification and regression applications. Finding the ideal hyperplane that categorizes the data points is how it operates. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. Here is an example of how SVM works : Suppose we have a dataset of students categorized into two classes based on their grades in two subjects, say Physics and Math. We want to predict whether a new student will pass or fail based on their grades in Physics and Math. We use SVM to identify the optimal hyperplane that separates the passing and failing students. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. The new student is then assigned to the class on the other side of the hyperplane. Support Vector Machin