Author: Rahul Khanna

MachineX: SVM as Non-Linear Classifiers

In our previous blogs, we have already looked and had a higher level understanding of SVM and why to choose SVM over other classifiers. In this blog post, we will look at a detailed explanation of how to use SVM for complex decision boundaries and build Non-Linear Classifiers using SVM. The primary method for doing this is by using Kernels. In linear SVM we find Continue Reading

MachineX: The inevitable Principal Component Analysis

In this blog post, we will look at an interesting feature extraction technique of Machine Learning known as Principal Component Analysis (PCA). PCA is one of the powerful techniques in dimensionality reduction, in fact, the de facto standard for human face recognition. Let’s first understand what is dimensionality reduction Dimensionality Reduction As an example let’s say we have a data set with many-many features(which is Continue Reading

MachineX: Cosine Similarity for Item-Based Collaborative Filtering

“A recommender system or a recommendation system (sometimes replacing “system” with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. “ – Wikipedia In simple terms a recommender system is where the system is capable of producing a list of recommendation with respect to an Continue Reading

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