What is 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.

What career opportunities can arise from learning scikit-learn?

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.

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.

How can taking online courses on Coursera help me learn 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.

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83 Ergebnisse für „scikit learn“

Coursera Project Network
Predict Sales Revenue with scikit-learn
Kompetenzen, die Sie erwerben: Machine Learning Algorithms, Statistical Programming, Regression, Accounting, Applied Machine Learning, Probability & Statistics, General Statistics, Scikit-Learn, Data Management, Financial Analysis, Data Analysis, Linearity, Python Programming, Machine Learning, Computer Programming, Linear Regression, Analysis
4.5
(225 Bewertungen)
Beginner · Guided Project · Less Than 2 Hours
University of Michigan
Applied Data Science with Python
Kompetenzen, die Sie erwerben: Algorithms, Applied Machine Learning, Artificial Neural Networks, Computational Logic, Computer Graphics, Computer Networking, Computer Programming, Data Analysis, Data Mining, Data Visualization, Econometrics, General Statistics, Graph Theory, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Mathematical Theory & Analysis, Mathematics, Matplotlib, Natural Language Processing, Network Analysis, Network Model, Plot (Graphics), Probability & Statistics, Programming Principles, Python Programming, Regression, Scikit-Learn, Statistical Machine Learning, Statistical Programming, Statistical Tests, Theoretical Computer Science
4.5
(32.2k Bewertungen)
Intermediate · Specialization · 3-6 Months
University of Michigan
Applied Machine Learning in Python
Kompetenzen, die Sie erwerben: Statistical Programming, Artificial Neural Networks, Computer Programming, Algorithms, Scikit-Learn, Econometrics, General Statistics, Regression, Probability & Statistics, Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Theoretical Computer Science, Python Programming
4.6
(8.1k Bewertungen)
Intermediate · Course · 1-4 Weeks

Suchanfragen mit Bezug zu scikit learn

scikit-learn model serving with online prediction using ai platform

predict employee turnover with scikit-learn

predict sales revenue with scikit-learn

multiple linear regression with scikit-learn

perform sentiment analysis with scikit-learn

support vector machines with scikit-learn

introduction to data science and scikit-learn in python

linear svm classification(soft margin) -using scikit learn

Zusammenfassend sind hier 10 unserer beliebtesten scikit learn Kurse

Predict Employee Turnover with scikit-learn: Coursera Project Network
Predict Sales Revenue with scikit-learn: Coursera Project Network
Multiple Linear Regression with scikit-learn: Coursera Project Network
Perform Sentiment Analysis with scikit-learn: Coursera Project Network
Support Vector Machines with scikit-learn: Coursera Project Network
Introduction to Data Science and scikit-learn in Python: LearnQuest
Scikit-learn Model Serving with Online Prediction Using AI Platform: Google Cloud
Linear SVM Classification(Soft Margin) -using Scikit Learn: Coursera Project Network
Predict Career Longevity for NBA Rookies using Scikit-learn: Coursera Project Network
Applied Data Science with Python: University of Michigan

Häufig gestellte Fragen zum Thema 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.‎

Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.