Linear regression is one of the most essential algorithms in machine learning, forming the backbone of predictive analytics. It models the relationship between a dependent variable (outcome) and one or more independent variables (predictors), allowing businesses and researchers to predict continuous outcomes based on input data. By identifying trends and relationships, linear regression provides meaningful ...
Machine learning offers numerous algorithms for making predictions and solving classification problems. One of the most widely used algorithms for classification tasks is logistic regression. Despite its name, logistic regression is not used for regression analysis; instead, it is applied to predict the probability of an event occurring, particularly in binary and multiclass classification tasks. ...
Machine learning models are often used to solve supervised learning tasks, particularly classification problems, where the goal is to assign data points to specific categories or classes. However, as datasets grow larger with more features, it becomes challenging for models to process the data effectively. This is where dimensionality reduction techniques like Linear Discriminant Analysis ...
Machine learning has revolutionized how we approach data-driven decision-making, with algorithms that allow machines to learn patterns and make predictions. Among the various algorithms, the decision tree stands out for its simplicity and effectiveness in both classification and regression tasks. Decision trees mimic human decision-making processes, making them intuitive to interpret and apply. This article ...
In machine learning, data is at the heart of everything. Machine learning models rely on vast amounts of data to make predictions and decisions. However, raw data can be difficult to interpret, especially when there’s a lot of it. This is where data visualization comes into play. Data visualization is the process of converting data ...
Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. As machine learning grows in importance across various industries, understanding the process involved in developing effective models becomes essential. This structured process is known as the Machine Learning Life Cycle. It ...
Neural networks have become the backbone of modern machine learning and artificial intelligence applications. From image recognition to natural language processing, neural networks are responsible for transforming large amounts of data into actionable insights. A critical element that determines the performance of neural networks is the activation function, which plays a key role in enabling ...
Machine learning has three main approaches: supervised, unsupervised, and semi-supervised learning. Supervised learning requires large amounts of labeled data, which can be costly and time-consuming, while unsupervised learning works with unlabeled data but may lack direction. Semi-supervised learning bridges the gap by using a small amount of labeled data along with a large amount of ...
Support Vector Machine (SVM) is a widely-used supervised learning algorithm for classification and regression tasks in machine learning. Known for its robustness and ability to handle both linear and non-linear data, SVM has applications in fields ranging from healthcare to finance. Whether it’s classifying images or detecting fraud, SVM offers a powerful solution by finding ...
Outliers are data points that significantly deviate from the rest of the dataset. These anomalies can arise due to measurement errors, data entry issues, or natural variations in data. In machine learning, outliers can disrupt model training by introducing noise, leading to skewed predictions and reduced accuracy. Detecting and handling outliers is a crucial step ...
Mohit Uniyal
Team Applied AI
