What Is scikit-learn?

In machine learning, scikit-learn is a gold-standard open-source data analysis library. Introduced in 2010, it's part of Python’s machine learning ecosystem. It allows for the implementation of a variety of machine learning and data modeling algorithms. It enables a concise, standardized model interface across all different models. Read on to learn more about scikit-learn, where to find a scikit-learn tutorial, and what types of careers use scikit-learn.

Types of scikit-learn

Scikit-learn offers a variety of algorithms to assist machine learning:

Supervised learning algorithms

Supervised learning algorithms have data that includes additional attributes the user wants to predict, such as classification or regression. This includes:

• Linear models: Intended for regression when the target value is expected to be linear

• Kernel ridge regression: Learns linear functions in the space induced by a kernel and data

• Support vector machines: Used for classification, regression, and outlier detection

• Stochastic gradient descent: Fits linear classifiers and regressors under convex loss functions

• Nearest neighbors: Provides functionality for neighbor-based learning methods

• Naive Bayes: Applies Bayes’ theorem to algorithms

Unsupervised learning algorithms

Unsupervised learning algorithms don’t include any set parameters and instead allow the algorithm to determine the contents of the data set. These include:

• Gaussian mixture models: Tests and estimates the performance of Gaussian models

• Manifold learning: Reduces non-linear dimensionality 

• Clustering: Clusters unlabeled data by function or class

• Novelty and outlier detection: Determines whether an observation exists within previous observations or without

Model selection and evaluation

Model selection and evaluation allow you to determine the best model for your particular data set. This includes: 

• Cross-validation: Uses a test set to prevent overfitting

• Validation curves: Creates a scoring model to evaluate for accuracy

• Metrics and scoring: Evaluate the quality of a model’s predictions

• Tuning the hyperparameters of estimators: Uses parameters that are not directly learned within estimators

What does scikit-learn do?

Scikit-learn integrates with many different Python libraries, including plotly and matplotlib for plotting, pandas dataframes, NumPy, SciPy, and more. It supports implementing various data models and machine learning algorithms through consistent Python APIs. Scikit-learn is easy to use, allowing you to define a predictive data model using only a few lines of code. It's an excellent tool for beginners and those looking to get their machine learning processes running quickly.

sklearn vs. scikit-learn

Sklearn is an abbreviation for scikit-learn and is the term used when you're installing the Python package scikit-learn, such as "python -m venv sklearn-env."

Who uses scikit-learn?

Scikit-learn is an open-source library that is used by a massive community of data professionals across the world. Some professions specifically focus on using scikit-learn as part of their machine learning tasks. These include:

Data scientists and machine learning engineers

Data scientists write applications that help analyze large data sets and identify hidden patterns. They create the algorithms necessary to organize and manage the information. Data scientists are well-versed in computer programming languages, using them to solve problems and make business recommendations.

Machine learning engineers use applications and programs to help improve human experiences. They use machine learning and write algorithms that help create efficient solutions for problems humans might have. Machine learning engineers create programs that learn on their own without the need for human supervision.

Academics and researchers

Academics and researchers use scikit-learn as part of their research methods, making it a valuable tool for graduate students and others looking for versatility and performance in an academic setting.

Business analysts

Business analysts use data analysis methods like scikit-learn to examine collected data for insights, solutions, and patterns. They then use this information to create recommendations that help their employer reach specific goals and metrics. They help businesses to become more efficient, productive, and competitive.

Pros and cons of using scikit-learn

Scikit-learn offers benefits and drawbacks to data professionals looking for an effective data tool. These include:

Benefits

Scikit-learn's benefits include its library of algorithms for foundational data analysis, such as clustering, regression, and classification. It is considered the go-to plain machine learning library for those who prefer to work with Python. It is beginner-friendly and easy to install, learn, and use, mainly because it includes its own scikit-learn tutorials.

Drawbacks

Scikit-learn does not offer deep learning capabilities, limiting some of its machine learning offerings.

How to get started in scikit-learn

If you’re interested in learning scikit-learn, the first step is to explore the robust resources available on the scikit-learn website. It has guides, tutorials, examples, and a community of users who can answer questions.

In general, the first step is to build a solid data science foundation if you’re interested in working within a field that uses scikit-learn, such as data analysis or machine learning. You might pursue software engineering, data science, or machine learning as a subject, but you’ll typically need a bachelor’s degree in a related field. You’ll want to have a firm grasp of Python.

If you’re new to the field, you’ll want to look for entry-level roles or other opportunities that allow you to gain hands-on experience with the different intricacies of Python and scikit-learn.

Learn more on Coursera

Scikit-learn is a plain Python library that many data professionals use to analyze and classify large data sets. 

Scikit-learn helps these professionals by providing access to various algorithms that perform different functions. If you’re interested in learning more about scikit-learn and general data modeling, explore the Coursera courses and certificates.

With options such as the University of Michigan’s Applied Machine Learning in Python course or the IBM Data Science Professional Certificate, you’ll learn about the foundations of programming and develop skills that may help you pursue roles in this exciting and evolving field. Learn more on Coursera today.