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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

    • U

      University of Amsterdam

      Basic Statistics

      Skills you'll gain: Descriptive Statistics, Statistical Hypothesis Testing, Sampling (Statistics), Probability Distribution, Correlation Analysis, Probability, Statistical Inference, Regression Analysis, Sample Size Determination, Statistics, Scientific Methods

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

      Beginner · Course · 1 - 3 Months

    • U

      University of Pennsylvania

      Fundamentals of Quantitative Modeling

      Skills you'll gain: Mathematical Modeling, Statistical Modeling, Regression Analysis, Business Modeling, Financial Modeling, Business Mathematics, Markov Model, Probability, Predictive Analytics, Process Optimization, Risk Management, Statistics, Probability Distribution, Simulation and Simulation Software, Forecasting

      4.6
      Rating, 4.6 out of 5 stars
      ·
      9.1K reviews

      Mixed · Course · 1 - 4 Weeks

    • U

      University of California San Diego

      Combinatorics and Probability

      Skills you'll gain: Combinatorics, Probability, Algorithms, Mathematical Modeling, Computational Thinking, Statistics, Game Theory, Python Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      862 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Alberta

      Reinforcement Learning

      Skills you'll gain: Reinforcement Learning, Machine Learning, Sampling (Statistics), Machine Learning Algorithms, Artificial Intelligence, Deep Learning, Simulations, Solution Architecture, Feature Engineering, Artificial Intelligence and Machine Learning (AI/ML), Markov Model, Supervised Learning, Algorithms, Performance Testing, Artificial Neural Networks, Pseudocode, Linear Algebra, Probability Distribution, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free
      Free
      U

      University of Amsterdam

      Data Analytics for Lean Six Sigma

      Skills you'll gain: Lean Six Sigma, Statistical Hypothesis Testing, Minitab, Regression Analysis, Data Visualization Software, Probability Distribution, Descriptive Statistics, Data Analysis, Statistical Analysis, Box Plots, Analytics, Process Improvement, Correlation Analysis, Variance Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.4K reviews

      Beginner · Course · 1 - 3 Months

    • D

      Duke University

      Inferential Statistics

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Inference, Statistical Reporting, Statistical Methods, R Programming, Statistical Software, Statistical Analysis, Probability & Statistics, Data Literacy, Sampling (Statistics), Probability Distribution, Software Installation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.7K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Illinois Urbana-Champaign

      Managerial Economics and Business Analysis

      Skills you'll gain: Descriptive Statistics, Supply And Demand, Market Dynamics, Sampling (Statistics), Statistical Inference, Business Analytics, Financial Systems, Financial Policy, Banking, Probability Distribution, Analytics, Statistical Analysis, Statistical Hypothesis Testing, Statistics, Regression Analysis, Microsoft Excel, Economics, Financial Market, Business Economics, Risk Management

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      4.1K reviews

      Beginner · Specialization · 3 - 6 Months

    • I

      Imperial College London

      Statistical Analysis with R for Public Health

      Skills you'll gain: Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Software, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Statistical Programming, Statistics, Statistical Methods, Public Health, Probability & Statistics, Epidemiology, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free
      Free
      U

      University of Zurich

      An Intuitive Introduction to Probability

      Skills you'll gain: Probability, Probability Distribution, Probability & Statistics, Descriptive Statistics, Applied Mathematics, Statistical Methods, Risk Analysis, Finance

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.8K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Alberta

      Fundamentals of Reinforcement Learning

      Skills you'll gain: Reinforcement Learning, Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Markov Model, Algorithms, Probability Distribution

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.8K reviews

      Intermediate · Course · 1 - 3 Months

    • R

      Rice University

      Business Statistics and Analysis

      Skills you'll gain: Statistical Hypothesis Testing, Microsoft Excel, Pivot Tables And Charts, Regression Analysis, Descriptive Statistics, Probability & Statistics, Graphing, Spreadsheet Software, Probability Distribution, Business Analytics, Statistical Analysis, Statistical Modeling, Excel Formulas, Data Analysis, Data Presentation, Statistics, Business Analysis, Statistical Methods, Sample Size Determination, Statistical Inference

      4.7
      Rating, 4.7 out of 5 stars
      ·
      13K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Pennsylvania

      Operations Analytics

      Skills you'll gain: Business Analytics, Descriptive Analytics, Predictive Analytics, Analytics, Demand Planning, Data-Driven Decision-Making, Operational Analysis, Business Operations, Risk Analysis, Forecasting, Operations Management, Simulation and Simulation Software, Process Optimization, Decision Making, Decision Tree Learning, Spreadsheet Software, Microsoft Excel, Probability Distribution

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.1K reviews

      Mixed · Course · 1 - 4 Weeks

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

    • Basic Statistics: University of Amsterdam
    • Fundamentals of Quantitative Modeling: University of Pennsylvania
    • Combinatorics and Probability: University of California San Diego
    • Reinforcement Learning: University of Alberta
    • Data Analytics for Lean Six Sigma: University of Amsterdam
    • Inferential Statistics: Duke University
    • Managerial Economics and Business Analysis: University of Illinois Urbana-Champaign
    • Statistical Analysis with R for Public Health: Imperial College London
    • An Intuitive Introduction to Probability: University of Zurich
    • Fundamentals of Reinforcement Learning: University of Alberta

    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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