Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize data. A picture is worth a thousand words. With the data visualized, it is easier for us to get some insights and decide on the next step in our machine learning models. In this tutorial, you will discover how to visualize data […]

Vector space models are to consider the relationship between data that are represented by vectors. It is popular in information retrieval systems but also useful for other purposes. Generally, this allows us to compare the similarity of two vectors from a geometric perspective. In this tutorial, we will see what is a vector space model and what it can do. After completing this tutorial, you will know: What is a vector space model and the properties of cosine similarity How […]

Singular value decomposition is a very popular linear algebra technique to break down a matrix into the product of a few smaller matrices. In fact, it is a technique that has many uses. One example is that we can use SVD to discover relationship between items. A recommender system can be build easily from this. In this tutorial, we will see how a recommender system can be build just using linear algebra techniques. After completing this tutorial, you will know: […]

Recent advance in machine learning has made face recognition not a difficult problem. But in the previous, researchers have made various attempts and developed various skills to make computer capable of identifying people. One of the early attempt with moderate success is eigenface, which is based on linear algebra techniques. In this tutorial, we will see how we can build a primitive face recognition system with some simple linear algebra technique such as principal component analysis. After completing this tutorial, […]

Last Updated on January 5, 2021 A lot of linear algebra is concerned with operations on vectors and matrices, and there are many different types of matrices. There are a few types of matrices that you may encounter again and again when getting started in linear algebra, particularity the parts of linear algebra relevant to machine learning. In this tutorial, you will discover a suite of different types of matrices from the field of linear algebra that you may encounter […]

Last Updated on August 15, 2020 You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper. In fact, if there was one area of mathematics I would suggest improving before the others, it would be linear algebra. It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms. In this […]

Last Updated on June 13, 2020 Machine learning data is represented as arrays. In Python, data is almost universally represented as NumPy arrays. If you are new to Python, you may be confused by some of the pythonic ways of accessing data, such as negative indexing and array slicing. In this tutorial, you will discover how to manipulate and access your data correctly in NumPy arrays. After completing this tutorial, you will know: How to convert your list data to […]

Last Updated on August 9, 2019 What is Linear Algebra? Linear algebra is a field of mathematics that is universally agreed to be a prerequisite to a deeper understanding of machine learning. Although linear algebra is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are practical for machine learning practitioners. With a solid foundation of what linear algebra is, it is possible to focus on just the good […]

Last Updated on August 9, 2019 Why Learn Linear Algebra for Machine Learning? Linear algebra is a field of mathematics that could be called the mathematics of data. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started in machine learning. This is misleading advice, as linear algebra makes more sense to a practitioner once they have a context of the applied machine learning process […]