Found 162 Articles for Data Science

Introduction One of the key regression techniques, multiple linear regression simulates the linear relationship between one continuous dependent variable and a number of independent variables. Two categories of linear regression algorithms exist − Simple − only addresses two features. Multiple − Deals with more than two features at once. Let's examine multiple linear regression in detail in this article. Multiple Linear Regression Multiple linear regression is a style of predictive analysis that is frequently used. You can comprehend the relationship between such a continuous dependent variable and two or more independent variables using this kind of analysis. The independent variables may be ... Read More

Introduction The rapidly expanding fields of artificial intelligence and machine learning are to thank for our machines' increasing intelligence and independence. But both fields are extremely complicated, and getting a greater understanding of them requires time and effort. The methods for regression and classification, which both predict in machine learning and employ labeled datasets, are called supervised learning algorithms. However, their point of departure is how they approach Machine Learning problems differently. Let's now examine Regression vs. Classification in greater detail. This article examines the definitions, kinds, distinctions, and application cases of regression vs. classification in machine learning. Regression ... Read More

Introduction We might picture sci-fi-inspired, humanoid automatons when we think about robots. Numerous different types of robots are in use today, even though these are still largely imagined. How will they transform the world, exactly? Robots are devices that possess the ability to sense, think, plan, and act independently. They can mimic human behavior and extend human capabilities and accomplish tasks independently. The word robot comes from the Czech word robota, which denotes forced labor. This article will examine the world of robots and how they may affect the future. Main Frame About Robotics The science of building ... Read More

Introduction The definition of manual is evolving in a world when almost all manual operations are mechanized. There are many different kinds of machine learning algorithms available today, some of which can help computers learn, get smarter, and resemble humans more. Because technology is advancing rapidly right now, it is possible to anticipate the future by looking at how computers have changed over time. Many different machine learning algorithms have been developed in these extremely dynamic times to aid in resolving difficult real-world problems. The automated, selfcorrecting ML algorithms will get better over time. Let's look at the various sorts ... Read More

Introduction Data encodings are unsupervised learned using an artificial neural network called an autoencoder. An autoencoder learns a lower-dimensional form (encoding) for a higher-dimensional data to learn a higher-dimensional data in a lower-dimensional form, frequently for dimensionality reduction. Autoencoders Autoencoders are very useful in the field of unsupervised machine learning. They can be used to reduce the data's size and compress it. Principle Component Analysis (PCA), which finds the directions along which data can be extrapolated with the least amount of variance, and autoencoders, which reconstruct our original input from a compressed version of it, differ from one another. If ... Read More

Introduction Kohonen proposed the idea of a self-organizing map (SOM) in the first place. Since it is an unsupervised neural network that is trained using unsupervised learning methods to create a low-dimensional, discretized representation from the input space of the training samples, it is a way to minimise the dimensions of the data. A map is a common name for this representation. This article will walk through a Kohonen Map beginner's guide, which is a well-known self-organizing map. To begin, let's define what self-organizing maps are. Self-Organizing Maps Self-organizing maps, also known as Kohonen maps or SOMs, are ... Read More

Introduction Machine learning models are essential for successful AI implementation since they represent the mathematical underpinnings of artificial intelligence. The quality of our AI depends entirely on the machine models that power it. We require a method for objectively evaluating our machine learning model's performance and deciding whether it is suitable for use. If we had a ROC curve, that would be useful. Everything that we need to learn about ROC curves is covered in this article. ROC Curve A Receiver Operating Characteristic (ROC) curve is a graphical representation of the performance of a binary classification model. It plots ... Read More

Introduction The learning component of artificial intelligence (AI) is indeed the focus of the area of machine learning. Algorithms that represent a set of data are used to create this learning component. To train machine learning models, certain datasets are sent through the algorithm. This article will define the term "Epoch, " which is used in machine learning, as well as other related topics like iterations, stochastic gradient descent. Anyone studying deep learning and machine learning or attempting to pursue a career in this industry must be familiar with these terms. Epoch in ML In machine learning, an epoch is ... Read More

Introduction Due to the rising need for qualified individuals, interesting job prospects, commercial applications, customization, and innovation, studying machine learning (ML) and artificial intelligence (AI) is becoming more and more crucial. Professionals who can design, construct, and maintain these systems are required as more businesses use AI and ML technology. In addition to providing interesting job prospects across a range of industries, ML and AI may assist organizations in streamlining operations, making data-driven choices, and increasing productivity and profitability. Moreover, ML and AI are at the forefront of technological advancement and may be utilized to tailor client experiences. People can ... Read More

Introduction In statistics, the phrase "overfitting" is used to describe a modeling error that happens when a function correlates too tightly to a certain set of data. As a result, overfitting could not be able to fit new data, which could reduce the precision of forecasting future observations. Examining validation measures like accuracy and loss might show overfitting. The validation measures frequently increase until a point at which they level out or start to drop when the model is affected by overfitting. During an upward trend, the model looks for a good match, and once it finds one, the movement ... Read More