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    Scikit-learn Courses Online

    Study Scikit-learn for machine learning in Python. Learn to build and evaluate models using this popular library.

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    Explore the Scikit-learn Course Catalog

    • C

      Coursera Project Network

      Scikit-Learn For Machine Learning Classification Problems

      Skills you'll gain: Scikit Learn (Machine Learning Library), Applied Machine Learning, Machine Learning Algorithms, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning

      4.5
      Rating, 4.5 out of 5 stars
      ·
      13 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • I

      IBM

      Python for Data Science, AI & Development

      Skills you'll gain: Jupyter, Automation, Web Scraping, Python Programming, Data Manipulation, Data Import/Export, Scripting, Data Structures, Data Processing, Data Collection, Application Programming Interface (API), Pandas (Python Package), Programming Principles, NumPy, Object Oriented Programming (OOP), Computer Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      41K reviews

      Beginner · Course · 1 - 3 Months

    • L

      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), Artificial Intelligence and Machine Learning (AI/ML), Statistical Methods, Programming Principles, Data Structures

      3.7
      Rating, 3.7 out of 5 stars
      ·
      49 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Applied Data Science with Python

      Skills you'll gain: Matplotlib, Network Analysis, Feature Engineering, Plot (Graphics), Data Visualization Software, Interactive Data Visualization, Pandas (Python Package), Applied Machine Learning, Supervised Learning, Text Mining, Scikit Learn (Machine Learning Library), Network Model, Jupyter, NumPy, Graph Theory, Data Manipulation, Natural Language Processing, Data Analysis, Data Processing, Unstructured Data

      4.5
      Rating, 4.5 out of 5 stars
      ·
      34K reviews

      Intermediate · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      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, Python Programming, Machine Learning, Random Forest Algorithm

      Beginner · Guided Project · Less Than 2 Hours

    • D
      S

      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Classification And Regression Tree (CART), Machine Learning Algorithms, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      33K reviews

      Beginner · Specialization · 1 - 3 Months

    • I

      IBM

      Machine Learning with Python

      Skills you'll gain: Supervised Learning, Feature Engineering, Jupyter, Unsupervised Learning, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Python Programming, Applied Machine Learning, Statistical Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Classification And Regression Tree (CART), Matplotlib, Regression Analysis, Random Forest Algorithm, Statistical Modeling, Data Manipulation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      17K reviews

      Intermediate · Course · 1 - 3 Months

    • U

      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, Unsupervised Learning, Classification And Regression Tree (CART), Dimensionality Reduction, Random Forest Algorithm, Regression Analysis, Artificial Neural Networks

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • D

      DeepLearning.AI

      Supervised Machine Learning: Regression and Classification

      Skills you'll gain: Supervised 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, Unsupervised Learning, Data-Driven Decision-Making

      4.9
      Rating, 4.9 out of 5 stars
      ·
      28K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Data Wrangling, Data Visualization, SQL, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Jupyter, Statistical Reporting, Exploratory Data Analysis, Data Mining, Data Cleansing, Matplotlib, Data Analysis, Unsupervised Learning, Generative AI, Pandas (Python Package), Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      142K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • G

      Google

      Crash Course on Python

      Skills you'll gain: Scripting, Python Programming, Computer Programming, Algorithms, Problem Management, Data Structures, Debugging, Development Environment

      4.8
      Rating, 4.8 out of 5 stars
      ·
      39K reviews

      Beginner · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Build a Machine Learning Web App with Streamlit and Python

      Skills you'll gain: Machine Learning Algorithms, Dashboard, Interactive Data Visualization, Data Visualization Software, Applied Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Web Applications, Predictive Modeling, Classification And Regression Tree (CART), Python Programming, Application Development, Pandas (Python Package)

      4.7
      Rating, 4.7 out of 5 stars
      ·
      403 reviews

      Intermediate · Guided Project · Less Than 2 Hours

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

    • Scikit-Learn For Machine Learning Classification Problems: Coursera Project Network
    • Python for Data Science, AI & Development: IBM
    • Introduction to Data Science and scikit-learn in Python: LearnQuest
    • Applied Data Science with Python: University of Michigan
    • Scikit-Learn to Solve Regression Machine Learning Problems: Coursera Project Network
    • Machine Learning: DeepLearning.AI
    • Machine Learning with Python: IBM
    • Applied Machine Learning in Python: University of Michigan
    • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
    • IBM Data Science: IBM

    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.‎

    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.‎

    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.‎

    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.‎

    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.

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