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Matrices and Other Data Science Concepts You need to Know

Matrices and Other Data Science Concepts You need to Know

Last Updated on June 3, 2022 by Editorial Team

Author(s): Rijul Singh Malik

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

A Blog about the basic concepts of data science

Photo by Photo Boards on Unsplash

What is a matrix?

A matrix is a rectangular arrangement of numbers, symbols, or expressions, usually arranged in rows and columns. A matrix is the fundamental data structure used in the field of linear algebra. A matrix is an arrangement of numbers or symbols in rows and columns. The number of rows and columns are the dimensions of the matrix. For example, the number of rows in a matrix is called it's rank. A matrix with two rows and three columns is called a 2×3 matrix.

In mathematics, a matrix is a rectangular array of numbers, symbols, or expressions, arranged in rows and columns. A matrix is a fundamental data structure used to store data and solve systems of linear equations. It is a 2-dimensional table of numbers, symbols, or expressions. The rows and columns are called matrix rows and matrix columns. Matrices can be real number matrices, complex number matrices, or even boolean matrices.

What is Principal Component Analysis (PCA)?

PCA is a simple yet powerful technique that allows you to transform a high-dimensional dataset into a low-dimensional dataset that is easy to interpret while preserving as much information as possible. It is often used in data visualization and data analytics. For example, you can use PCA to create a 2D scatter plot of the first two principal components of a high-dimensional dataset and easily see patterns in the data.

PCA is one of the most fundamental data science techniques (and one of the most important, too!). It’s the very first analytical technique that data science undergraduates learn. It can be used to reduce the dimensionality of data (from thousands of features to a few), and, then, it can be used to summarize the data. It’s a very powerful technique that can be used to solve a wide range of problems in engineering, business, and even science.

What is unsupervised learning?

Unsupervised learning is a type of machine learning that attempts to find hidden insights in unlabeled data. Unsupervised learning is used in situations where there is not a clear way to label the data. This often happens when you have data that is not fully labeled and you need to find hidden patterns or groupings in the data. An example of this would be figuring out which social media platform is most popular among your users. How do you find this out without any direct information? You can use unsupervised learning to find it out.

What is a distribution?

A distribution is a way to graphically display the probability of values in a dataset. Each dataset has a unique distribution, which is why it is important to understand the data you are working with. There are many different types of distributions, including normal, uniform, exponential, and many more. A distribution is a graphical representation of a dataset using a curve or a collection of points. The x-axis of the graph represents the independent variable of the dataset and the y-axis is the dependent variable. The curve is often interpreted as the probability that a given value will occur in the dataset. For example, imagine a dataset of the number of people from each state that are in the army. The dataset might include the numbers of people from California, New York, and many other states.

What is deep learning?

Deep learning is a subset of machine learning. In other words, deep learning is an artificial intelligence process that allows computers to learn tasks by analyzing data. Deep learning is a type of neural network that employs multiple layers of artificial neurons to make predictions. Some examples of deep learning include image identification, speech recognition, and natural language understanding. The deep learning process works in a manner similar to the human brain. For example, when a person wants to learn a new language, the brain processes many examples of a language and then starts to use the information to understand new examples. For computers to learn, they need to be fed sets of data called training sets. Training sets are used to train the artificial intelligence process and make predictions on new sets of data.

What is Bayesian statistics?

A Bayesian network, Bayes network, belief network, Bayes(ian) model, or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables, showing the relationships among them and their conditional independencies. Probabilistic graphical models are a general framework for statistical models that can represent different types of dependency structures.

What is a Markov Chain?

A Markov Chain is a type of stochastic process that can be used to model a wide range of systems. These processes are often used in a variety of mathematical and computer science applications, including computer simulations and machine learning algorithms. Markov Chains can be used to model the randomness of events, which is important in a huge number of applications. For example, they can be used to model stock market price fluctuations and word transitions in a sentence.

A Markov chain is a probability model that predicts the behavior of a system over a period of time. The system is assumed to depend only on its present state and not on the sequence of events that preceded it. The concept was introduced by Andrey Markov in his paper “The theory of possible worlds,” which was published in 1929. Markov chains are useful in a wide range of applications, such as modeling the behavior of a robot, a device, or a person. In a Markov chain, the system will be in a particular state at a particular time. The system transitions from one state to the next based on a probability distribution that depends on the current state. The Markov chain allows us to determine the probability that the system will be in a specific state at a specific time. We can use this probability to represent the system in a graph.

What is a Markov Model?

A Markov model is a stochastic process that can be used to model any situation where future events are dependent on past events. It is often used to model processes where the current state of the system depends only on its previous state and not on any other information. Markov models are used in a variety of domains, including psychology, statistics, machine learning, and bioinformatics. The most common type of Markov model is the Markov chain, which is a random process that has a finite number of states. As time advances, the model moves from one state to another with a certain probability.

What is a Markov Process?

Breadth-first search is an algorithm for traversing or searching tree or graph data structures. It starts at the root node and explores the neighbors of that node first, before moving to the next node, and so on, until all nodes in the graph are explored. It is a special case of the depth-first search algorithm. Both of these algorithms are examples of graph search algorithms. Depth-first search explores as far as possible along each branch before backtracking, while breadth-first search explores as far as possible along each branch except for the branch that was just explored.

Most people think of a “process” as something that happens over time. When you make a cup of coffee, for example, you start by pouring water into a pot, adding the coffee grounds, and then heating the water on a stove. You might think of a Markov process as a series of steps or a sequence of events over time. A Markov process is a sequence of random variables that are not dependent on the previous random variables. If you have a Markov process, you have a sequence of random variables that are dependent only on their own values. The Markov property is closely related to a concept in probability called a Markov chain, which is used to describe an infinite sequence of random variables. A Markov property is only dependent on the current value of the random variable, not the previous value. The “butterfly effect” is a sequence of events in which a small change in one state can cause a large change in another state. A good example of a butterfly effect is how a butterfly flapping its wings in one part of the world can cause a hurricane in another part of the world.

What is a Markov Random Field?

Markov Random Field (MRF) is an undirected graphical model used in machine learning and statistical modeling. Markov random fields were first introduced by Andrey Markov in 1929. The MRF for a set of random variables “X” = {X1, …, Xn} on a probability space (Ω, F, P) is a graph Mn = (V, E) where E ⊆ V × V is the set of edges.

What is a Hidden Markov Model?

A hidden Markov model (HMM) is a statistical model used to find the probability of a system/event/process having a specific outcome. The model uses a sequence of random variables, which are called states in the context of HMMs, with each state being a particular outcome of the process. The state transitions are usually shown as a graph where the nodes represent the states and the edges represent the transitions between the states. These models are often used to analyze processes where the preceding state of a system cannot be directly observed, but the distribution of its future states can. They are also used to model systems where the order in which the system generated its outcomes is irrelevant. Hidden Markov models are used in many applications like speech recognition and language modeling for machine translation.


Knowing these concepts can take you a long way into data science. Thinking of starting a new career as a data scientist? Check out this blog.

Matrices and Other Data Science Concepts You need to Know was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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