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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

    • S

      Stanford University

      Introduction to Statistics

      Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Data Collection, Probability Distribution

      4.6
      Rating, 4.6 out of 5 stars
      ·
      4.1K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google

      Process Data from Dirty to Clean

      Skills you'll gain: Data Cleansing, Sampling (Statistics), Data Integrity, Data Quality, Data Validation, Sample Size Determination, Data Analysis, Data Manipulation, SQL, Data Transformation, Spreadsheet Software

      4.8
      Rating, 4.8 out of 5 stars
      ·
      18K reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      Scalable Machine Learning on Big Data using Apache Spark

      Skills you'll gain: Apache Spark, PySpark, Applied Machine Learning, Big Data, Machine Learning Methods, Data Storage, Data Pipelines, Machine Learning Algorithms, Distributed Computing, Data Processing, Exploratory Data Analysis, Statistical Analysis

      3.8
      Rating, 3.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • V

      Vanderbilt University

      Data Management for Clinical Research

      Skills you'll gain: Clinical Data Management, Data Capture, Clinical Research, Data Management, Data Collection, Data Sharing, Data Strategy, Data Quality, Sampling (Statistics), Data Integrity, Data Validation, Regulatory Compliance

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.8K reviews

      Beginner · Course · 1 - 3 Months

    • I

      IIMA - IIM Ahmedabad

      Pre-MBA Statistics

      Skills you'll gain: Sampling (Statistics), Probability, Probability & Statistics, Statistical Hypothesis Testing, Statistics, Data Literacy, Statistical Inference, Estimation, Statistical Analysis, Descriptive Statistics, Data Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      280 reviews

      Beginner · Course · 1 - 3 Months

    • N

      Northwestern University

      Fundamentals of Digital Image and Video Processing

      Skills you'll gain: Image Analysis, Digital Communications, Computer Vision, Data Processing, Visualization (Computer Graphics), Medical Imaging, Electrical and Computer Engineering, Motion Graphics, Linear Algebra, Color Theory, Bayesian Statistics, Applied Mathematics, Sampling (Statistics), Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.8K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      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

    • U

      University of London

      Get Interactive: Practical Teaching with Technology

      Skills you'll gain: Education Software and Technology, Learning Management Systems, Collaborative Software, Test Tools, Content Management Systems, Security Software, Video Production

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.6K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      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 Trial
      Free Trial
      D

      Duke University

      Data Analysis with R

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

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      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

    • Status: Free Trial
      Free Trial
      W

      Wesleyan University

      Data Analysis and Interpretation

      Skills you'll gain: Regression Analysis, Data Storytelling, Classification And Regression Tree (CART), Statistical Hypothesis Testing, Descriptive Statistics, Data Presentation, Statistical Analysis, Statistical Software, Correlation Analysis, Exploratory Data Analysis, Data Manipulation, Decision Tree Learning, Data Management, Data Analysis, Predictive Modeling, Data Visualization, Data Literacy, Random Forest Algorithm, SAS (Software), Statistical Modeling

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.4K reviews

      Beginner · Specialization · 3 - 6 Months

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    1…567…33

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

    • Introduction to Statistics: Stanford University
    • Process Data from Dirty to Clean: Google
    • Scalable Machine Learning on Big Data using Apache Spark: IBM
    • Data Management for Clinical Research: Vanderbilt University
    • Pre-MBA Statistics: IIMA - IIM Ahmedabad
    • Fundamentals of Digital Image and Video Processing: Northwestern University
    • Managerial Economics and Business Analysis: University of Illinois Urbana-Champaign
    • Get Interactive: Practical Teaching with Technology: University of London
    • Statistical Analysis with R for Public Health: Imperial College London
    • Data Analysis with R: Duke University

    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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