Gradient Descent is one of the most important optimization algorithms in the field of machine learning. Optimization algorithms are used to minimize or maximize a function, which is crucial for training models effectively. Gradient Descent helps find the best parameters (weights and biases) for a model by reducing the error in predictions step by step. ...
Artificial Intelligence (AI) is a vast field that focuses on creating machines capable of performing tasks that typically require human intelligence. Two critical areas of AI are machine learning (ML) and deep learning (DL). These technologies have revolutionized industries like healthcare, finance, and entertainment. But what exactly sets them apart? Both machine learning and deep ...
Machine learning has revolutionized numerous industries, providing systems the ability to learn from data and improve over time without explicit programming. One of the earliest and most fundamental algorithms in machine learning is the Perceptron model. Developed in the late 1950s by Frank Rosenblatt, the Perceptron is historically significant for laying the groundwork for neural ...
Convolutional Neural Networks (CNNs) are a type of deep learning model commonly used in image recognition tasks. Unlike traditional neural networks, CNNs are designed to automatically detect patterns from images, making them highly efficient in visual data processing. Deep learning, a subset of machine learning, enables machines to mimic the way humans learn from experience, ...
In machine learning, when building models for classification tasks (like predicting whether an email is spam or not), it’s important to evaluate how well the model performs. One of the most useful tools for doing this is the ROC curve. ROC stands for Receiver Operating Characteristic, and this curve helps visualize a model’s ability to ...
Machine learning is all about teaching computers to learn from data and make decisions without being explicitly programmed. One of the key tasks in machine learning is concept learning, which focuses on learning general ideas or concepts from specific examples. By learning these concepts, machines can classify new data accurately, making this technique essential for ...
Clustering is a key technique in machine learning, widely used for finding patterns and grouping similar data points. It belongs to unsupervised learning, meaning that it works without labeled data or predefined categories. Instead, clustering automatically identifies natural groupings within a dataset based on certain characteristics, such as distance or similarity. This makes it especially ...
Collaborative filtering is a core technique used in recommendation systems. It plays a crucial role in personalizing experiences for users on platforms such as e-commerce sites, streaming services, and social media networks, improving engagement by suggesting relevant items based on user behavior patterns. What is Collaborative Filtering? Collaborative filtering is a method used to predict ...
In machine learning, improving model accuracy and reducing errors are critical objectives. One approach to achieve this is through ensemble methods, which combine the predictions of multiple models to create a more robust and accurate final model. Rather than relying on a single model, ensemble techniques harness the collective intelligence of many models, each contributing ...
Machine learning (ML) is a subset of artificial intelligence (AI) that allows computers to learn from data and make predictions or decisions without being explicitly programmed. By training on large datasets, machine learning models can identify patterns, relationships, and anomalies in data, enabling automation and intelligent decision-making in various industries. Today, machine learning models are ...
Mohit Uniyal
Team Applied AI
