What skills or experience do I need to already have, before starting to learn scikit-learn?

Before starting to learn scikit-learn, you should have experience in and a sound understanding of Python. It is also often required to have experience in additional Python libraries, including NumPy, Scipy, Joblib, Matplotlib, and pandas.

What are the benefits of taking an online Scikit Learn course?

Online Scikit Learn courses offer a convenient and flexible way to enhance your knowledge or learn new Scikit Learn skills. Choose from a wide range of Scikit Learn courses offered by top universities and industry leaders tailored to various skill levels.

Best Scikit Learn Courses & Certificates [2026]

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Scikit Learn

Scikit Learn Courses

Scikit Learn courses can help you learn data preprocessing, model selection, and evaluation techniques, along with supervised and unsupervised learning methods. You can build skills in feature engineering, hyperparameter tuning, and implementing algorithms like decision trees and support vector machines. Many courses also introduce tools such as Jupyter Notebooks and Python libraries, that support applying machine learning concepts and visualizing data to derive actionable insights.

Popular Scikit Learn Courses and Certifications

Status: Free Trial
IBM
Machine Learning with Python
Skills you'll gain: Unsupervised Learning, Supervised Learning, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Dimensionality Reduction, Python Programming, Statistical Analysis, Classification And Regression Tree (CART), Feature Engineering
4.7
Rating, 4.7 out of 5 stars
18K reviews
Intermediate · Course · 1 - 3 Months
Coursera
Scikit-Learn For Machine Learning Classification Problems
Skills you'll gain: Scikit Learn (Machine Learning Library), Applied Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning, Data Analysis
4.6
Rating, 4.6 out of 5 stars
18 reviews
Beginner · Guided Project · Less Than 2 Hours
Status: New
Status: Free Trial
Coursera
Machine Learning with Scikit-learn, PyTorch & Hugging Face
Skills you'll gain: Generative AI, Supervised Learning, Generative Model Architectures, Unsupervised Learning, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Data Collection, Machine Learning Algorithms, OpenAI, Feature Engineering, Data Ethics, Dimensionality Reduction, MLOps (Machine Learning Operations), Machine Learning, Multimodal Prompts, Data Processing, Network Architecture
4.6
Rating, 4.6 out of 5 stars
12 reviews
Intermediate · Professional Certificate · 3 - 6 Months
Status: Free Trial
University of Michigan
Applied Machine Learning in Python
Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Dimensionality Reduction, Random Forest Algorithm, Regression Analysis
4.6
Rating, 4.6 out of 5 stars
8.6K reviews
Intermediate · Course · 1 - 4 Weeks
Status: New
Status: Free Trial
EDUCBA
NumPy & Pandas: Analyze & Transform Data
Skills you'll gain: Pandas (Python Package), NumPy, Data Wrangling, Data Transformation, Data Manipulation, Pivot Tables And Charts, Data Cleansing, Data Analysis, Numerical Analysis, Data Structures, Descriptive Statistics
Mixed · Course · 1 - 4 Weeks
Status: Free Trial
LearnQuest
Introduction to Data Science and scikit-learn in Python
Skills you'll gain: Feature Engineering, Exploratory Data Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data Manipulation, NumPy, Data Analysis, Python Programming, Regression Analysis, Predictive Modeling, Machine Learning, Classification And Regression Tree (CART), Data Science, Statistical Hypothesis Testing, Supervised Learning, Statistical Methods, Programming Principles, Data Structures
3.7
Rating, 3.7 out of 5 stars
52 reviews
Beginner · Course · 1 - 4 Weeks

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Coursera
Scikit-Learn to Solve Regression Machine Learning Problems
Skills you'll gain: Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Machine Learning Algorithms, Applied Machine Learning, Predictive Analytics, Python Programming, Machine Learning, Data Analysis, Random Forest Algorithm
4.8
Rating, 4.8 out of 5 stars
8 reviews
Beginner · Guided Project · Less Than 2 Hours
Status: Free Trial
IBM
Data Analysis with Python
Skills you'll gain: Exploratory Data Analysis, Data Wrangling, Data Transformation, Data Analysis, Data Cleansing, Data Manipulation, Data Import/Export, Predictive Modeling, Regression Analysis, Statistical Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data-Driven Decision-Making, Matplotlib, Feature Engineering, Data Visualization, Data Pipelines, NumPy, Python Programming
4.7
Rating, 4.7 out of 5 stars
20K reviews
Intermediate · Course · 1 - 3 Months
Status: Free Trial
Packt
Mastering Machine Learning Algorithms using Python
Skills you'll gain: Data Science, Unsupervised Learning, Exploratory Data Analysis, Probability & Statistics, Machine Learning Algorithms, Applied Machine Learning, Classification And Regression Tree (CART), Data Analysis, Python Programming, Random Forest Algorithm, Dimensionality Reduction, Predictive Modeling, NumPy, Regression Analysis, Statistical Analysis, Data Processing, Deep Learning, Pandas (Python Package), Data Visualization, Data Manipulation
Intermediate · Specialization · 3 - 6 Months
Status: New
Status: Preview
Packt
Machine Learning with PyTorch and Scikit-Learn
Skills you'll gain: Reinforcement Learning, Dimensionality Reduction, PyTorch (Machine Learning Library), Deep Learning, Generative AI, Pandas (Python Package), Scikit Learn (Machine Learning Library), Python Programming, Machine Learning, Artificial Neural Networks, Data Processing, Natural Language Processing, Feature Engineering, Predictive Modeling, Supervised Learning, Unsupervised Learning, Data Transformation, NumPy
Intermediate · Course · 3 - 6 Months
Status: Free Trial
IBM
Tools for Data Science
Skills you'll gain: Jupyter, Computer Programming Tools, Data Visualization Software, Data Science, GitHub, R (Software), Big Data, R Programming, Statistical Programming, Machine Learning, Cloud Computing, Git (Version Control System), IBM Cloud, Development Environment, Other Programming Languages, Version Control, Query Languages, Python Programming, Open Source Technology
4.5
Rating, 4.5 out of 5 stars
30K reviews
Beginner · Course · 1 - 3 Months
Status: Free Trial
DeepLearning.AI
Supervised Machine Learning: Regression and Classification
Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Feature Engineering, Artificial Intelligence, Classification And Regression Tree (CART), Python Programming, Regression Analysis, Statistical Modeling, Data Transformation
4.9
Rating, 4.9 out of 5 stars
31K reviews
Beginner · Course · 1 - 4 Weeks

Searches related to scikit learn

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scikit-learn to solve regression machine learning problems

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In summary, here are 10 of our most popular scikit learn courses

Machine Learning with Python: IBM
Scikit-Learn For Machine Learning Classification Problems: Coursera
Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
Applied Machine Learning in Python: University of Michigan
NumPy & Pandas: Analyze & Transform Data: EDUCBA
Introduction to Data Science and scikit-learn in Python: LearnQuest
Scikit-Learn to Solve Regression Machine Learning Problems: Coursera
Data Analysis with Python: IBM
Mastering Machine Learning Algorithms using Python: Packt
Machine Learning with PyTorch and Scikit-Learn: Packt

Frequently Asked Questions about Scikit Learn

Scikit-learn—or skilearn—is a very useful library of algorithms in Python for machine learning. It started out as a Google summer of code project in 2007 then was further developed by a group of data scientists from the French Institute for Research in Computer Science and Automation (FIRCA) and released to the public in 2010. The scikit-learn library lives at a github.com URL and is now a community effort that anyone with qualified skills can contribute to. While the library is primarily written in Python, it's also built on Python-based libraries that include NumPy, Matplotlib, pandas, and SciPy. It gives users tools for statistical modeling and machine learning, such as classification, clustering, regression, model selection, preprocessing, and dimensionality reduction.‎

When you learn scikit-learn, you put yourself in a position to be able to contribute to and help maintain the scikit-learn library. Top contributors include data scientists, software developers, machine learning researchers, research scientists, and open-source developers. Outside of contributing to the library, individuals in these career fields and others related to machine learning are better equipped to perform their job duties when they've learned how to utilize the algorithms in scikit-learn.‎

Taking online courses on Coursera can help you learn about Python and machine learning as well as the specifics of using and creating algorithms for scikit-learn. You might learn how to build univariate and multivariate linear regression models using scikit-learn, use pandas to manage data, and perform exploratory data analysis and data visualization with seaborn, a Python library based on Matplotlib. With Coursera's online courses, you may also have the opportunity to make predictions in specific topics like electricity consumption, sentiment analysis, and career longevity for NBA rookies, for example.‎

When looking to enhance your workforce's skills in Scikit Learn, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.