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    • Random Forest

    Random Forest Courses Online

    Study random forest algorithms for machine learning. Learn to build and apply random forest models for classification and regression tasks.

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    Explore the Random Forest Course Catalog

    • C

      Coursera Project Network

      Interpretable Machine Learning Applications: Part 1

      Skills you'll gain: Feature Engineering, Classification And Regression Tree (CART), Decision Tree Learning, Applied Machine Learning, Random Forest Algorithm, Predictive Modeling, Data Import/Export, Data Analysis, Machine Learning, Data-Driven Decision-Making, Regression Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      52 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      U

      University of Maryland, College Park

      Framework for Data Collection and Analysis

      Skills you'll gain: Surveys, Research Methodologies, Data Collection, Research Design, Data Quality, Data Analysis, Data Validation, Sampling (Statistics), Big Data, Statistical Methods

      4.2
      Rating, 4.2 out of 5 stars
      ·
      764 reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      Universidad de los Andes

      Fundamentos de estadística aplicada

      Skills you'll gain: Statistical Hypothesis Testing, Descriptive Statistics, Statistical Methods, Data Analysis, Regression Analysis, Sampling (Statistics), Statistical Analysis, Probability & Statistics, Probability Distribution, Applied Mathematics, Statistical Inference

      4.5
      Rating, 4.5 out of 5 stars
      ·
      134 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      P

      Packt

      Building and Training Neural Networks with PyTorch

      Skills you'll gain: PyTorch (Machine Learning Library), Artificial Neural Networks, Image Analysis, Deep Learning, Computer Vision, Classification And Regression Tree (CART), Predictive Modeling, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, Algorithms

      4.9
      Rating, 4.9 out of 5 stars
      ·
      8 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Illinois Urbana-Champaign

      Machine Learning Algorithms with R in Business Analytics

      Skills you'll gain: Exploratory Data Analysis, R Programming, Analytics, Business Analytics, Statistical Machine Learning, Applied Machine Learning, Predictive Modeling, Unsupervised Learning, Regression Analysis, Machine Learning, Classification And Regression Tree (CART), Predictive Analytics, Data Analysis, Supervised Learning

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      38 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Black-box and White-box Testing

      Skills you'll gain: Cucumber (Software), Gherkin (Scripting Language), Software Testing, Testability, Test Case, Behavior-Driven Development, Code Coverage, Acceptance Testing, Unit Testing, Functional Testing, Test Automation, Requirements Analysis, Java Programming

      3.8
      Rating, 3.8 out of 5 stars
      ·
      110 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      UX Research at Scale: Surveys, Analytics, Online Testing

      Skills you'll gain: Surveys, UI/UX Research, User Research, Survey Creation, Sampling (Statistics), A/B Testing, Qualitative Research, Usability Testing, Research Methodologies, Web Analytics and SEO, Data Collection, Research Design, Sample Size Determination, Analytics, Data Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      205 reviews

      Beginner · Course · 1 - 3 Months

    • P

      Pontificia Universidad Católica de Chile

      Introducción a los modelos de demanda de transporte

      Skills you'll gain: Data Collection, Transportation Operations, Surveys, Quantitative Research, Mathematical Modeling, Predictive Modeling, Statistical Methods, Regression Analysis, Spatial Analysis, Probability Distribution, Probability

      4.7
      Rating, 4.7 out of 5 stars
      ·
      459 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Data Analysis in R: Predictive Analysis with Regression

      Skills you'll gain: Ggplot2, Data Visualization, Regression Analysis, Predictive Analytics, Data-Driven Decision-Making, Statistical Modeling, R Programming, Descriptive Statistics, Exploratory Data Analysis, Statistics

      4.2
      Rating, 4.2 out of 5 stars
      ·
      13 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • T

      The Chinese University of Hong Kong

      Information Theory

      Skills you'll gain: Digital Communications, Theoretical Computer Science, Telecommunications, Information Management, Probability, Probability Distribution, Technical Communication, Algorithms, General Mathematics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      163 reviews

      Mixed · Course · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      L

      LearnQuest

      Machine Learning Models in Science

      Skills you'll gain: Applied Machine Learning, Data Processing, Dimensionality Reduction, Data Cleansing, Machine Learning Algorithms, Data Transformation, Artificial Neural Networks, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Unsupervised Learning, Supervised Learning, Predictive Modeling, Python Programming

      4
      Rating, 4 out of 5 stars
      ·
      12 reviews

      Intermediate · Course · 1 - 4 Weeks

    • É

      École Polytechnique

      Aléatoire : une introduction aux probabilités - Partie 1

      Skills you'll gain: Probability, Probability Distribution, Simulations, Probability & Statistics, Statistical Methods, Mathematical Modeling, Mathematical Theory & Analysis, Applied Mathematics

      4.5
      Rating, 4.5 out of 5 stars
      ·
      105 reviews

      Mixed · Course · 1 - 3 Months

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    In summary, here are 10 of our most popular random forest courses

    • Interpretable Machine Learning Applications: Part 1: Coursera Project Network
    • Framework for Data Collection and Analysis: University of Maryland, College Park
    • Fundamentos de estadística aplicada: Universidad de los Andes
    • Building and Training Neural Networks with PyTorch: Packt
    • Machine Learning Algorithms with R in Business Analytics: University of Illinois Urbana-Champaign
    • Black-box and White-box Testing: University of Minnesota
    • UX Research at Scale: Surveys, Analytics, Online Testing: University of Michigan
    • Introducción a los modelos de demanda de transporte: Pontificia Universidad Católica de Chile
    • Data Analysis in R: Predictive Analysis with Regression: Coursera Project Network
    • Information Theory: The Chinese University of Hong Kong

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Reinforcement Learning (13)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Random Forest

    Random forest is a classification algorithm that is a collection of various decision trees. It is a classification algorithm that, with the combination of trees, helps increase the overall results. Random forest is used for classification and regression tasks and shows how many uncorrelated pieces can produce more accurate predictions than the individual ones.‎

    Random forest is important to learn because it will help you advance in your data-related career. It will give you skills to perform more accurate tests and help you achieve results with a low prediction error. It is also important to learn random forest because it is widely used and helps you maintain the accuracy of large data even with missing variables. Learning random forest will save you time while providing better, more accurate results.‎

    Some typical careers that use random forest are data scientists and analytic jobs. In these careers, you will use random forest to analyze data and come up with predictions based on the results. The data gathered and analyzed can be from many different areas. This can include medical data to predict diseases or illnesses, market data to predict sales, or use data to predict the number of cars rented by season, for example. In an analytic job and as a data scientist you will use random forest to come up with accurate predictions.‎

    Online courses will help you learn about random forest because they will offer video lectures, readings, and examples to explain the material to you. These courses will give you the chance to practice and demonstrate your knowledge with various assignments or projects on different software. Online courses will also help you learn random forest by giving you the flexibility to study on your own time while having access to the material and experts that will guide you along the course.‎

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

    When looking to enhance your workforce's skills in Random Forest, 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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