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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
    Free Trial
    I

    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering

    4.7
    Rating, 4.7 out of 5 stars
    ·
    18K reviews

    Intermediate · Course · 1 - 3 Months

  • C

    Coursera

    Scikit-Learn For Machine Learning Classification Problems

    Skills you'll gain: Scikit Learn (Machine Learning Library), Classification Algorithms, Applied Machine Learning, Machine Learning Algorithms, 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
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    Learn & Build Machine Learning Models with Python

    Skills you'll gain: Matplotlib, NumPy, Pandas (Python Package), Model Evaluation, Data Preprocessing, Regression Analysis, Scikit Learn (Machine Learning Library), Data Manipulation, Machine Learning, Feature Engineering, Exploratory Data Analysis, Applied Machine Learning, Supervised Learning, Python Programming

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Applied Machine Learning in Python

    Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Random Forest Algorithm, Regression Analysis, Classification Algorithms, Artificial Neural Networks

    4.6
    Rating, 4.6 out of 5 stars
    ·
    8.8K reviews

    Intermediate · Course · 1 - 4 Weeks

  • C

    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, Classification Algorithms, Machine Learning, Data Analysis

    4.8
    Rating, 4.8 out of 5 stars
    ·
    8 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    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, Classification Algorithms, Data Preprocessing, Data Analysis, Python Programming, Regression Analysis, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Data Science, Statistical Hypothesis Testing, Supervised Learning, Statistical Methods, Data Structures

    3.7
    Rating, 3.7 out of 5 stars
    ·
    57 reviews

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    Data Analysis with Python

    Skills you'll gain: Exploratory Data Analysis, Data Analysis, Data Import/Export, Data Manipulation, Predictive Modeling, Data Preprocessing, Model Evaluation, Data Science, Predictive Analytics, Pandas (Python Package), Statistical Modeling, Regression Analysis, Feature Engineering, Statistical Analysis, Matplotlib, Scikit Learn (Machine Learning Library), Data Visualization, NumPy, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
    ·
    20K reviews

    Intermediate · Course · 1 - 3 Months

  • Next level skills. New Year savings.

    Save on Coursera Plus
  • Status: New
    New
    Status: Preview
    Preview
    P

    Packt

    Machine Learning with PyTorch and Scikit-Learn

    Skills you'll gain: Reinforcement Learning, Dimensionality Reduction, PyTorch (Machine Learning Library), Machine Learning Algorithms, Data Preprocessing, Model Evaluation, Artificial Intelligence and Machine Learning (AI/ML), Generative Adversarial Networks (GANs), Machine Learning Methods, Deep Learning, Transfer Learning, Applied Machine Learning, Pandas (Python Package), Scikit Learn (Machine Learning Library), Python Programming, Machine Learning, Artificial Neural Networks, Data Processing, Natural Language Processing, Feature Engineering

    Intermediate · Course · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    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, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning

    4.9
    Rating, 4.9 out of 5 stars
    ·
    32K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    Machine Learning with Python: Build & Optimize

    Skills you'll gain: Matplotlib, Data Preprocessing, Regression Analysis, Scikit Learn (Machine Learning Library), Data Visualization, Python Programming, Applied Machine Learning, Pandas (Python Package), Model Evaluation, NumPy, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Data Manipulation, Dimensionality Reduction, Statistical Methods, Data Transformation, Feature Engineering, Unsupervised Learning, Performance Tuning

    Mixed · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    Python: Logistic Regression & Supervised ML

    Skills you'll gain: Model Evaluation, Feature Engineering, Supervised Learning, Exploratory Data Analysis, Classification Algorithms, Machine Learning Algorithms, Applied Machine Learning, Decision Tree Learning, Logistic Regression, Predictive Modeling, Data Analysis, Scikit Learn (Machine Learning Library), Data Preprocessing, Pandas (Python Package), NumPy, Data Cleansing

    4.4
    Rating, 4.4 out of 5 stars
    ·
    16 reviews

    Mixed · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Preview
    Preview
    C

    Coursera

    Databricks Machine Learning Fundamentals

    Skills you'll gain: Model Deployment, Databricks, MLOps (Machine Learning Operations), Apache Spark, Applied Machine Learning, PySpark, Data Preprocessing, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Scikit Learn (Machine Learning Library), Feature Engineering, Application Deployment, Model Evaluation, Real Time Data, Exploratory Data Analysis, Engineering

    Intermediate · Course · 1 - 4 Weeks

What brings you to Coursera today?

Searches related to scikit learn

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1234…10

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
  • Learn & Build Machine Learning Models with Python: EDUCBA
  • Applied Machine Learning in Python: University of Michigan
  • Scikit-Learn to Solve Regression Machine Learning Problems: Coursera
  • Introduction to Data Science and scikit-learn in Python: LearnQuest
  • Data Analysis with Python: IBM
  • Machine Learning with PyTorch and Scikit-Learn: Packt
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Machine Learning with Python: Build & Optimize: EDUCBA

Frequently Asked Questions about Scikit Learn

Scikit-learn is a powerful open-source machine learning library for Python, designed to facilitate the implementation of machine learning algorithms and data analysis. It provides simple and efficient tools for data mining and data analysis, making it an essential resource for anyone looking to work in data science or machine learning. Its importance lies in its ability to streamline the process of building predictive models, enabling users to focus on the insights derived from data rather than the complexities of the underlying algorithms.‎

With skills in scikit-learn, you can pursue various roles in the tech and data science sectors. Common job titles include Data Scientist, Machine Learning Engineer, Data Analyst, and AI Researcher. These positions often require a solid understanding of machine learning principles and the ability to apply them using scikit-learn to solve real-world problems. As businesses increasingly rely on data-driven decision-making, the demand for professionals skilled in scikit-learn continues to grow.‎

To effectively learn scikit-learn, you should have a foundational understanding of Python programming, as the library is built on this language. Familiarity with basic statistics and linear algebra is also beneficial, as these concepts underpin many machine learning algorithms. Additionally, knowledge of data manipulation libraries like NumPy and pandas will enhance your ability to preprocess and analyze data before applying machine learning techniques.‎

Some of the best online courses for learning scikit-learn include the Introduction to Data Science and scikit-learn in Python and the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These courses provide comprehensive coverage of machine learning concepts and practical applications using scikit-learn, making them ideal for both beginners and those looking to enhance their skills.‎

Yes. You can start learning scikit-learn on Coursera for free in two ways:

  1. Preview the first module of many scikit-learn courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in scikit-learn, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn scikit-learn effectively, start by familiarizing yourself with Python and its data manipulation libraries. Next, enroll in online courses that focus on scikit-learn, such as those mentioned earlier. Practice by working on real datasets and projects to apply what you've learned. Engaging with the community through forums and study groups can also enhance your understanding and keep you motivated.‎

Typical topics covered in scikit-learn courses include data preprocessing, model selection, evaluation metrics, supervised and unsupervised learning algorithms, and techniques for improving model performance. You will also learn about feature engineering and how to handle different types of data, which are crucial for building effective machine learning models.‎

For training and upskilling employees, courses like the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate are excellent choices. These programs are designed to provide a comprehensive understanding of machine learning concepts and practical applications, making them suitable for organizations looking to enhance their workforce's skills in data science and machine learning.‎

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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