Scikit-learn

 Scikit-learn is an open source data analysis library, and the gold standard for Machine Learning (ML) in the Python ecosystem. Key concepts and features include:

• Algorithmic decision-making methods, including:
  • Classification: identifying and categorizing data based on patterns.
  • Regression: predicting or projecting data values based on the average mean of existing and planned data.
  • Clustering: automatic grouping of similar data into datasets.
• Algorithms that support predictive analysis ranging from simple linear regression to neural network pattern recognition.
• Interoperability with NumPy, pandas, and matplotlib libraries.

ML is a technology that enables computers to learn from input data and to build/train a predictive model without explicit programming. ML is a subset of Artificial Intelligence (AI). 

Why Use Scikit-Learn For Machine Learning

Whether you are just looking for an introduction to ML, want to get up and running fast, or are looking for the latest ML research tool, you will find that scikit-learn is both well-documented and easy to learn/use. As a high-level library, it lets you define a predictive data model in just a few lines of code, and then use that model to fit your data. It’s versatile and integrates well with other Python libraries, such as matplotlib for plotting, numpy for array vectorization, and pandas for dataframes.