In machine learning, the quality of your model is heavily influenced by the data it is trained on. One essential step in data preprocessing is ensuring that the data is properly scaled to improve model performance. This is where normalization comes into play. Normalization is a technique used to scale numerical data features into a ...
In machine learning, algorithms rely on data to learn patterns and make predictions. However, raw data is rarely ready for direct use by these models. Data preprocessing is a critical step that can significantly affect the performance of machine learning models. Among the various preprocessing techniques, feature scaling is one of the most important. Feature ...
Machine learning models are built on a foundation of data and algorithms that process, analyze, and extract insights from that data. Behind the scenes, many of these machine learning algorithms rely heavily on mathematical concepts to function effectively. One of the most important mathematical fields for machine learning is **linear algebra**. Linear algebra deals with ...
In machine learning, models need to make informed decisions based on data. For this, they rely on methods to measure uncertainty and randomness within a dataset. One of the key concepts used to quantify this uncertainty is entropy. Derived from information theory, entropy helps machine learning algorithms determine how to split data most effectively, thereby ...
In machine learning, raw data in its initial form often contains noise, irrelevant information, or excessive dimensionality, making it challenging to use directly in models. This is where feature extraction plays a crucial role. It involves transforming raw data into a more informative and usable format, which enhances model performance and reduces computational costs. For ...
In machine learning, models rely heavily on the features of a dataset to make accurate predictions. However, more data does not always lead to better results. This is where feature selection becomes critical. It is a process that involves selecting a subset of the most relevant features, helping to improve model performance, reduce training time, ...
Hierarchical clustering is a powerful unsupervised machine learning algorithm used to group data points into a hierarchy of clusters. It is particularly useful when the number of clusters is not predefined, and it helps to visualize the data’s structure through a dendrogram, which represents the nested clustering relationships. Hierarchical clustering finds applications across various domains, ...
In machine learning, data is the foundation upon which models are built. However, raw data often contains inconsistencies, missing values, or irrelevant information that can affect model performance. This is where data preprocessing becomes essential. Data preprocessing is the process of preparing and transforming raw data into a format that can be easily used by ...
Machine learning models aim to make accurate predictions by learning from data. However, two critical factors—bias and variance—affect the performance of these models. Understanding and balancing these factors is essential for building models that generalize well to new data. Bias refers to errors due to overly simplistic assumptions in the learning algorithm, while variance measures ...
K-Means Clustering is an unsupervised learning algorithm used to group data points into distinct clusters based on similarity. It’s widely applied in tasks like market segmentation, image compression, and anomaly detection, known for its simplicity, efficiency, and scalability in handling large datasets. What is K-Means Clustering? K-Means Clustering is an unsupervised learning algorithm that divides ...
Mayank Gupta
Team Applied AI
Abhimanyu Saxena