Fundamentals of Machine Learning

Fundamentals of Machine Learning Glossary #

Fundamentals of Machine Learning Glossary

Activation Function #

A mathematical function that introduces non-linearity to neural networks, allowing them to learn complex patterns in data. Common examples include sigmoid, tanh, ReLU, and softmax functions.

Artificial Intelligence (AI) #

The simulation of human intelligence processes by machines, especially computer systems. AI encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding.

Backpropagation #

An algorithm used in training artificial neural networks to update the weights of the network by calculating the gradient of the loss function with respect to the weights.

Batch Size #

The number of training examples utilized in one iteration (epoch) of training in a machine learning model.

Bias #

A term in machine learning that represents a model's tendency to consistently learn the wrong thing by not taking into account all the information in the data.

Classification #

A type of supervised learning where the goal is to predict the categorical class labels of new instances based on past observations.

Clustering #

An unsupervised learning technique used to group data points into clusters based on similarities in the data.

Convolutional Neural Network (CNN) #

A type of deep neural network commonly used for analyzing visual imagery. CNNs have convolutional layers that automatically learn hierarchical patterns in the data.

Cross #

Validation: A technique used to evaluate the performance of a machine learning model by splitting the data into multiple subsets for training and testing.

Deep Learning #

A subfield of machine learning that focuses on learning representations of data through multiple layers of neural networks.

Dimensionality Reduction #

The process of reducing the number of input variables in a dataset by obtaining a set of principal variables.

Dropout #

A regularization technique used in neural networks to prevent overfitting by randomly disabling a fraction of neurons during training.

Ensemble Learning #

A machine learning technique that combines multiple models to improve the overall performance and robustness of the system.

Feature Engineering #

The process of selecting, combining, and transforming variables in a dataset to improve the performance of machine learning algorithms.

Gradient Descent #

An optimization algorithm used to minimize the loss function by iteratively moving in the direction of the steepest descent.

Hyperparameter #

Configurable variables that dictate the learning process of a machine learning algorithm, such as learning rate, batch size, and number of epochs.

Kernel #

A function used to transform input data into a higher-dimensional space to make it linearly separable for classification tasks.

Logistic Regression #

A statistical model used for binary classification that predicts the probability of a binary outcome.

Loss Function #

A function that quantifies the difference between the predicted values of a model and the actual values in the training data.

Neural Network #

A computational model inspired by the human brain that consists of interconnected nodes (neurons) organized in layers.

Overfitting #

A phenomenon in machine learning where a model learns noise in the training data instead of the underlying patterns, leading to poor generalization on unseen data.

Principal Component Analysis (PCA) #

A dimensionality reduction technique that transforms the data into a new coordinate system to capture the maximum variance in the data.

Reinforcement Learning #

A type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.

Regularization #

Techniques used to prevent overfitting by adding a penalty term to the loss function that discourages complex models.

Regression #

A type of supervised learning where the goal is to predict continuous output values based on input features.

Stochastic Gradient Descent #

A variant of the gradient descent algorithm that updates the weights of a model using a small subset of training examples at a time.

Support Vector Machine (SVM) #

A supervised learning algorithm used for classification and regression tasks by finding the hyperplane that best separates the classes in the data.

Underfitting #

A situation where a machine learning model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test sets.

Unsupervised Learning #

A type of machine learning where the goal is to find hidden patterns or intrinsic structures in the data without using labeled examples.

Validation Set #

A subset of data used to tune hyperparameters and evaluate the performance of a machine learning model during training.

Variance #

A measure of how much the predictions of a model vary for different training sets, indicating the model's sensitivity to changes in the training data.

Word Embedding #

A technique used to represent words as dense vectors in a continuous vector space, capturing semantic relationships between words based on their context.

This glossary provides a comprehensive overview of key concepts and terms in mac… #

By understanding these fundamental principles, learners can effectively apply machine learning techniques to solve real-world problems in their field.