Glossary

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  • What is Artificial Intelligence?

    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.

  • What is Binary Classification?

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

  • What is Business Intelligence?

    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.

  • What is CatBoost?

    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.

  • What is Clustering?

    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

  • What is Data Science?

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

  • What is DataFrame?

    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.

  • What is Decision Tree?

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

  • What is Ensemble 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.

  • What is Jupyter Notebook?

    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.

  • What is Machine Learning Pipeline?

    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.

  • What is Machine Learning?

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

  • What is Python Package?

    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.

  • What is Python Virtual Environment?

    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.

  • What is Random Forest?

    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.

  • What is Regression?

    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.

  • What is SVM?

    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.