Introduction to Data Science and Machine Learning

These flashcards cover key concepts surrounding Data Science and Machine Learning, including definitions and explanations of methodologies, algorithms, and real-world applications.

Data Science

A field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Machine Learning

A subset of artificial intelligence that enables machines to learn and improve from experience and identify patterns in data to make predictions.

Supervised Learning

A type of machine learning that uses labeled data to train models to predict labels for new, unlabeled data.

Unsupervised Learning

A type of machine learning that involves training models with unlabeled data to find patterns or structure in the data.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions based on rewards or punishments from the environment.

K-Means Clustering

An unsupervised learning algorithm that partitions data into k clusters based on similarity.

Decision Trees

A supervised learning algorithm used for classification and regression that represents decisions and their possible consequences in a tree-like structure.

Linear Regression

A supervised learning algorithm used to predict a continuous output variable based on one or more predictor variables.

Principal Component Analysis (PCA)

A technique that transforms high-dimensional data into a lower-dimensional space while preserving as much of the original information as possible.

Black-box Models

Machine learning models where the internal workings are not visible or interpretable to the user.

White-box Models

Models that are interpretable, allowing users to understand how the model makes predictions, such as decision trees.

CRISP-DM

A data mining process model that describes the different stages in the data mining life cycle.

Automated Decision-Making

The use of algorithms and machine learning models to make predictions or decisions with minimal human intervention.

Data Preparation

The process of cleaning and transforming raw data into a format suitable for analysis or modeling.

Algorithmic Trading

The use of machine learning and algorithms to make trading decisions in financial markets.

Human Resource Recruitment Bot

A virtual recruitment assistant that automates candidate screening and interactions, enhancing the recruitment process.

Fraud Detection

The application of machine learning to identify and prevent fraudulent activities by analyzing transaction patterns.