Statistics, a fundamental tool in data analysis, is divided into two main branches: descriptive statistics and inferential statistics. Descriptive statistics summarizes raw data through measures like mean, median, and standard deviation, offering a clear picture of what the data reveals. However, this method only describes the observed dataset without extending beyond it. Inferential statistics goes ...
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 ...
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 ...
The AdaBoost algorithm, short for Adaptive Boosting, is a popular method in machine learning that belongs to the family of ensemble learning techniques. Ensemble learning combines multiple models, often referred to as “weak learners,” to create a strong, accurate model. Adaboost specifically focuses on improving the performance of weak learners (models that perform slightly better ...
Ensemble learning is a popular approach in machine learning where multiple models are combined to improve the accuracy and robustness of predictions. Often, individual models may have limitations, such as overfitting or underfitting. By combining several models, ensemble methods can reduce these issues and produce better results. Stacking (stacked generalization) is an ensemble technique that ...
Preparing for a machine learning interview can be challenging, especially for those new to the field. To help you succeed, this guide covers over 100 machine learning interview questions, categorized by experience level: Beginner, Intermediate, and Advanced. Whether you’re starting your journey or advancing your career, these questions provide a solid foundation across various machine ...
In machine learning, statistical models often rely on hidden information or latent variables—elements of data that are not directly observed but influence the overall outcomes. Identifying the optimal parameters for these models becomes challenging when such latent variables are present. The Expectation-Maximization (EM) algorithm offers a powerful solution to this problem. It is designed to ...
Quantum Machine Learning (QML) is an exciting and emerging field that combines quantum computing and machine learning. While classical machine learning has made great strides, it faces limitations, especially when solving highly complex problems that require enormous computational power. This is where QML comes into play—it uses the principles of quantum mechanics to potentially solve ...
Artificial Intelligence (AI) has rapidly become one of the most transformative technologies of the 21st century. From virtual assistants like Siri and Alexa to recommendation systems on Netflix and Amazon, AI is everywhere. Its applications span across industries such as healthcare, finance, transportation, and retail, making it an essential skill for future professionals. Whether you’re ...
Anshuman Singh
Mohit Uniyal
Team Applied AI