Glossary

Artificial Intelligence is the simulation of human intelligence processes by machines, particularly computer systems, to perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making.

AutoML automates the end-to-end process of applying machine learning to real-world problems, encompassing data preprocessing, feature selection, model selection, hyperparameter tuning, and evaluation to enhance efficiency and accessibility.

Binary Classification is a fundamental task in Machine Learning where the goal is to classify input data into one of two categories or classes.

Business Intelligence is a set of processes, technologies, and tools that enable organizations to gather, store, analyze, and visualize data to support decision-making and strategic planning. It involves transforming raw data into actionable insights that can inform business strategies, improve operational efficiency, identify opportunities, and mitigate risks.

CatBoost is a machine learning library developed by Yandex, designed for gradient boosting on decision trees. It's known for its efficient handling of categorical features without preprocessing, making it a popular choice for datasets with mixed data types.

Clustering is a data analysis technique that groups similar data points together into clusters based on certain features or characteristics. It helps other models to prepare dataset for supervised learning algorithms

A Data Engineer is a professional responsible for designing, constructing, and maintaining the systems and architecture that allow for the collection, storage, processing, and analysis of large volumes of data. They play a crucial role in enabling organizations to leverage data for decision-making, reporting, and analytics.

Data Science is the field of extracting insights and knowledge from data using scientific methods, algorithms, and processes to inform decision-making.

A DataFrame is a two-dimensional, labeled data structure in Python's pandas library, resembling a spreadsheet or SQL table, where columns can be of different types.

Decision Tree is a hierarchical, supervised model in a tree-like shape. It is widely used in Machine Learning.

Ensemble Learning combines multiple models for better performance. Bagging trains models independently and averages their predictions, while boosting corrects errors sequentially. Popular methods include Random Forests, AdaBoost, and XGBoost.

A GBM is an ensemble technique for regression and classification, built sequentially by combining predictions of weak learners, typically shallow decision trees. It results in a highly accurate, robust model capable of handling complex datasets.

Hyperparameter Tuning selects the optimal hyperparameters, set before training and not learned from data, to improve model performance on validation data, balancing underfitting and overfitting.

An .ipynb file is a notebook document used by Jupyter Notebook, an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.

Jupyter Notebook is an open-source web application to create and share documents containing live code, equations, visualizations, and narrative text. Used widely in data science, education etc. However it can be even better.

LightGBM (Light Gradient Boosting Machine) is a fast, efficient gradient boosting framework by Microsoft. It uses a histogram-based algorithm and leaf-wise tree growth for high accuracy and speed, supporting large datasets, missing values, and categorical features.

A Machine Learning Pipeline is a series of sequential steps that are taken to process and analyze data in order to build and deploy a machine learning model. These steps typically include data preprocessing, feature engineering, model selection, training, evaluation, and deployment.

Machine Learning is a subset of Artificial Intelligence (AI) focused on learning from given data, categorizing it, and generalizing predictions.

A Parquet file is a columnar storage file format optimized for use with big data processing frameworks like Apache Hadoop and Spark, providing efficient data compression, encoding, and high-performance querying for large datasets.

A Python Package Manager is a tool that automates the process of installing, upgrading, configuring, and managing software packages written in Python. The most commonly used Python Package Manager is `pip`.

A Python Package is a collection of Python modules grouped together to provide related functionality. Packages allow for modular programming, where you can organize code into separate, logical unitsand can be easily distributed.

Python Pandas is an open-source library for data manipulation and analysis in Python. It provides powerful data structures and tools for manipulating numerical tables and time series, cleaning, transforming, and analyzing data efficiently.

A Python Virtual Environment is a self-contained directory that houses its own Python installation and dependencies, allowing you to isolate and manage project-specific packages separately from the system-wide Python installation.

A Random Forest is an ensemble learning technique that builds multiple decision trees during training and outputs the mode of the classes (classification) or the average prediction (regression) of the individual trees.

Regression is one of the main applications of the supervised Machine Learning. Like in statistics Regression in ML is used to search for association between independent variables.

SVM stands for Support Vector Machine, which is a supervised learning algorithm used for classification and regression tasks. The primary goal of SVM is to find the hyperplane that best separates the data points into different classes.

Time series analysis involves analyzing time-ordered data to extract meaningful statistics and patterns, enabling forecasting of future values based on historical data. It's essential in finance, weather forecasting, and many other fields.

XGBoost (Extreme Gradient Boosting) is an optimized machine learning algorithm for classification and regression tasks, known for its high performance, efficiency, scalability, and flexibility in handling large datasets and complex models.