Generalization in machine learning refers to a model’s ability to perform well on new, unseen data after being trained on a specific dataset. It determines how effectively a model applies learned patterns to make accurate predictions beyond the training data. A well-generalized model captures meaningful relationships within the data, ensuring reliability across different scenarios. However, ...
Computational Learning Theory (CLT) is a branch of machine learning and theoretical computer science that studies the mathematical principles behind learning algorithms. It focuses on defining how efficiently an algorithm can learn patterns from data and generalize to unseen inputs. CLT provides a formal framework for evaluating machine learning models, answering key questions such as: ...
Maximum Likelihood Estimation (MLE) is a statistical technique used to estimate the parameters of a probability distribution by maximizing the likelihood function. It is widely applied in machine learning, statistics, and AI to optimize models for tasks such as classification, regression, and generative modeling. MLE is commonly used in logistic regression, Gaussian Mixture Models (GMMs), ...
Image classification is a key task in computer vision that involves assigning labels to images based on their content. It enables machines to automatically recognize and categorize objects, patterns, and scenes, making it an essential technology in healthcare, security, retail, and autonomous systems. Machine learning (ML) plays a crucial role in automating image classification, eliminating ...
Image recognition is a computer vision technology that enables machines to identify, classify, and interpret visual content from images or videos. By using machine learning (ML) and deep learning, image recognition allows computers to detect objects, recognize patterns, and extract insights from visual data with human-like accuracy. This technology is revolutionizing industries by enabling automation ...
Standardization in machine learning is a preprocessing technique used to transform numerical features so that they have a mean of zero and a standard deviation of one. This ensures that all features contribute equally to the model, preventing bias caused by different scales of measurement. Standardization is crucial for improving model performance, especially in algorithms ...
Model selection in machine learning is the process of identifying the most suitable algorithm for a given dataset to achieve optimal accuracy, efficiency, and generalization. Since different models have unique strengths and weaknesses, selecting the right one is crucial for ensuring reliable predictions and scalable AI solutions. Choosing an appropriate model directly impacts performance metrics, ...
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data, recognize patterns, and make predictions without being explicitly programmed. By using algorithms that improve through experience, machine learning has transformed industries, powering applications like fraud detection, recommendation systems, medical diagnosis, and autonomous vehicles. The significance of machine learning lies ...
Azure Machine Learning (Azure ML) is a cloud-based platform designed by Microsoft to simplify and accelerate the development, training, and deployment of machine learning models. It provides a robust ecosystem that enables data scientists, AI engineers, and businesses to build and manage machine learning workflows with ease. As part of the Microsoft Azure AI ecosystem, ...
Machine learning (ML) is transforming industries by enabling data-driven decision-making, automation, and predictive analytics. With numerous algorithms available, selecting the right one for a given problem is critical for model accuracy and efficiency. This cheat sheet provides a quick overview of key ML algorithms, helping both beginners and professionals understand their applications, strengths, and limitations. ...
Team Applied AI
Abhimanyu Saxena
