The below glossary provides a broad overview of key terms in the field of data science and machine learning, covering various concepts, techniques, and algorithms used in data analysis, machine learning, and artificial intelligence.
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đ¤ Artificial Intelligence (AI): Branch of computer science that aims to create machines capable of intelligent behavior. AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics.
đ Big Data: Term used to describe large, complex datasets that are difficult to manage and analyze using traditional data processing methods. Big data is characterized by volume, velocity, variety, and veracity.
đ Clustering: Unsupervised learning technique that involves grouping similar data points together to form clusters or segments based on their intrinsic characteristics.
đ Computer Vision: Field of artificial intelligence that enables computers to interpret and understand visual information from the real world, such as images and videos.
đ Data Analysis: Process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
âī¸ Data Mining: Process of discovering patterns, trends, and insights from large datasets using statistical techniques, machine learning algorithms, and computational methods.
đ Data Science: Interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data.
đ§ Deep Learning: Subset of machine learning that uses neural networks with many layers (deep architectures) to learn complex patterns in large amounts of data. It has achieved remarkable success in tasks such as image recognition and natural language processing.
đ Decision Tree: Supervised learning algorithm that partitions the feature space into a hierarchy of binary decisions, forming a tree-like structure that can be used for classification or regression tasks.
đ¨ Dimensionality Reduction: Process of reducing the number of features in a dataset while preserving its important information. Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for dimensionality reduction.
đ Ensemble Learning: Technique that combines multiple machine learning models to improve prediction accuracy, robustness, and generalization performance.
đ ī¸ Feature Engineering: Process of selecting, transforming, and creating new features (variables) from raw data to improve the performance of machine learning models.
đ ī¸ Feature Extraction: Process of extracting relevant information or features from raw data to represent the data in a more meaningful and compact form.
đŧī¸ Generative Adversarial Network (GAN): Deep learning architecture consisting of two neural networks, a generator and a discriminator, trained adversarially to generate realistic synthetic data samples. GANs have applications in image generation, data augmentation, and unsupervised learning.
đ Gradient Boosting: Ensemble learning technique that builds a strong predictive model by combining multiple weak models sequentially, with each new model focusing on the errors of the previous ones.
đ° Hierarchical Clustering: Unsupervised learning algorithm that creates a hierarchy of clusters by recursively merging or splitting data points based on their similarity or dissimilarity.
đ K-means Clustering: Unsupervised learning algorithm that partitions data points into a specified number of clusters based on their distance to the centroids of the clusters.
đ§ Long Short-Term Memory (LSTM): Specialized type of recurrent neural network architecture that is capable of learning long-term dependencies in sequential data. LSTMs are widely used in tasks requiring memory and context, such as speech recognition and language translation.
đ Machine Learning: Subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can learn patterns from data and make predictions or decisions.
đ Model Evaluation: Process of assessing the performance and effectiveness of machine learning models using various metrics and techniques.
đ Natural Language Generation (NLG): Branch of artificial intelligence that focuses on generating human-like text from structured data or other forms of input. NLG techniques are used in chatbots, virtual assistants, and automated report generation.
đ Natural Language Processing (NLP): Field of artificial intelligence that focuses on the interaction between computers and human languages. NLP techniques enable computers to understand, interpret, and generate human language.
đ Natural Language Understanding (NLU): Branch of artificial intelligence that focuses on understanding and interpreting human language. NLU techniques are used in tasks such as sentiment analysis, named entity recognition, and text classification.
đ§ Neural Network: Computational model inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) organized in layers. Neural networks are capable of learning complex patterns and relationships from data.
đ¯ Overfitting: Phenomenon where a machine learning model performs well on the training data but fails to generalize to unseen data due to capturing noise or irrelevant patterns.
đ Principal Component Analysis (PCA): Dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. PCA identifies orthogonal axes (principal components) that capture the most significant variability in the data.
đŽ Predictive Analytics: Branch of data analysis that focuses on predicting future outcomes or trends based on historical data and statistical modeling techniques.
đ˛ Random Forest: Ensemble learning method that constructs multiple decision trees during training and outputs the mode or mean prediction of the individual trees for classification or regression tasks.
đ Recurrent Neural Network (RNN): Deep learning architecture designed for processing sequential data, such as time series or natural language. RNNs use recurrent connections between neurons to capture temporal dependencies in the data.
đ Regularization: Technique used to prevent overfitting by adding a penalty term to the loss function, encouraging the model to learn simpler patterns.
đšī¸ Reinforcement Learning: Type of machine learning where an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties.
đ Sentiment Analysis: Natural language processing task that involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text. Sentiment analysis is used to analyze social media posts, customer reviews, and other text data sources.
đ¯ Support Vector Machine (SVM): Supervised learning algorithm that finds the optimal hyperplane that separates data points into different classes with the maximum margin of separation.
đ¯ Supervised Learning: Type of machine learning where the algorithm learns from labeled data, with each example consisting of input features and a corresponding target variable.
đ Term Frequency-Inverse Document Frequency (TF-IDF): Statistical measure used to evaluate the importance of a word in a document relative to a collection of documents. TF-IDF assigns higher weights to terms that are frequent in a document but rare in the overall document collection.
đ Text Classification: Natural language processing task that involves categorizing text documents into predefined classes or categories based on their content. Text classification is used in spam detection, topic modeling, and sentiment analysis.
đ°ī¸ Time Series Analysis: Statistical technique that involves analyzing and modeling sequential data points collected over time. Time series analysis is used in forecasting, trend analysis, and anomaly detection in various domains, including finance, healthcare, and manufacturing.
đ¯ Transfer Learning: Machine learning technique where a model trained on one task is reused or adapted for a different but related task. Transfer learning leverages knowledge learned from previous tasks to improve performance on new tasks with limited labeled data.
đ¯ Underfitting: Phenomenon where a machine learning model is too simple to capture the underlying structure of the data, resulting in poor performance on both training and test datasets.
đ Unsupervised Learning: Type of machine learning where the algorithm learns patterns from unlabeled data, seeking to find hidden structure or relationships within the dataset.
đ Word Embedding: Technique used to represent words as dense, low-dimensional vectors in a continuous vector space. Word embeddings capture semantic relationships between words and are used as input features for natural language processing tasks.