A Hands-on Introduction to Machine Learning - Python Basics

Vocabulary flashcards covering the introduction to Python, its environment, basic structures, and essential libraries for data science.

Python

A scripting language available on every platform that is easy to learn, easy to use, extensible, and robust.

IDE

Stands for Integrated Development Environment; examples mentioned include Eclipse, Jupyter notebook, Anaconda, and Spyder.

Arithmetic operators

One of the basic components of Python used for performing mathematical calculations.

Logical operators

Basic components of Python used for performing logical operations in scripts.

Control structures

Programming elements including condition checking with ‘if’ and ‘else’, while loops, and for loops.

Numpy

A library used for storing a set of numbers, performing descriptive analysis, and visualization with bar graphs.

Pandas

A library used for loading external data, plotting data, and performing correlation analysis.

Matplotlib

Listed as one of the most useful libraries for data science alongside numpy, pandas, and sklearn.

Sklearn

One of the most useful libraries for practicing data science with Python.

Jupyter

One of the most common tools for practicing Python, identified as an Integrated Development Environment (IDE).

Anaconda

A common tool and Integrated Development Environment (IDE) used for practicing Python in data science.