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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

      Universidade de São Paulo

      Estatística não-paramétrica para a tomada de decisão

      Skills you'll gain: Sample Size Determination, Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Statistical Methods, Data-Driven Decision-Making, Quantitative Research, Statistical Analysis, Decision Making, Probability Distribution

      4.4
      Rating, 4.4 out of 5 stars
      ·
      138 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      CertNexus

      Build Decision Trees, SVMs, and Artificial Neural Networks

      Skills you'll gain: Random Forest Algorithm, Decision Tree Learning, Deep Learning, Applied Machine Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Algorithms, Statistical Machine Learning, Supervised Learning, Computer Vision, Regression Analysis, Natural Language Processing

      4.9
      Rating, 4.9 out of 5 stars
      ·
      13 reviews

      Intermediate · Course · 1 - 3 Months

    • D

      Duke University

      Financial Risk Management with R

      Skills you'll gain: Financial Market, Risk Management, Market Data, Risk Analysis, Probability Distribution, Financial Data, R Programming, Financial Modeling, Portfolio Management, Statistical Programming, Financial Analysis, Securities (Finance), Time Series Analysis and Forecasting, Probability & Statistics, Statistical Modeling

      Build toward a degree

      4.3
      Rating, 4.3 out of 5 stars
      ·
      248 reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of London

      Statistics for International Business

      Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Data Presentation, Statistics, Estimation, Probability, Data-Driven Decision-Making, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing, Probability Distribution, Data Visualization, Data Analysis, Histogram, Graphing

      3.8
      Rating, 3.8 out of 5 stars
      ·
      307 reviews

      Mixed · 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

    • Status: Free
      Free
      P

      Peking University

      医学统计学与SPSS软件(基础篇)

      Skills you'll gain: Statistical Hypothesis Testing, SPSS, Statistical Analysis, Statistical Methods, Sampling (Statistics), Descriptive Statistics, Correlation Analysis, Regression Analysis, Probability & Statistics, Statistical Inference, Data Analysis Software

      4.6
      Rating, 4.6 out of 5 stars
      ·
      152 reviews

      Mixed · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Applied Machine Learning

      Skills you'll gain: PyTorch (Machine Learning Library), Unsupervised Learning, Computer Vision, Machine Learning Algorithms, Applied Machine Learning, Image Analysis, Dimensionality Reduction, Supervised Learning, Reinforcement Learning, Feature Engineering, Regression Analysis, Data Cleansing, Machine Learning, Data Mining, Scikit Learn (Machine Learning Library), Statistical Machine Learning, Advanced Analytics, Deep Learning, Artificial Neural Networks, Decision Tree Learning

      3.7
      Rating, 3.7 out of 5 stars
      ·
      7 reviews

      Intermediate · Specialization · 3 - 6 Months

    • U

      University of Colorado Boulder

      Classification Analysis

      Skills you'll gain: Data Analysis, Supervised Learning, Classification And Regression Tree (CART), Machine Learning Algorithms, Data Science, Predictive Modeling, Feature Engineering, Data Mining, Machine Learning, Bayesian Statistics, Probability & Statistics

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      D

      Duke University

      Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital

      Skills you'll gain: Image Analysis, Computer Vision, Digital Communications, Computer Graphics, Visualization (Computer Graphics), Medical Imaging, Applied Mathematics, Spatial Analysis, Advanced Mathematics, Linear Algebra, Matlab, Mathematical Modeling, Algorithms, Probability Distribution

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.1K reviews

      Mixed · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Random Processes

      Skills you'll gain: Probability & Statistics, Probability Distribution, Simulations, Statistical Modeling, Estimation, Correlation Analysis, Engineering Analysis, Statistical Analysis, Reliability, Engineering, Spatial Analysis

      Mixed · Course · 1 - 4 Weeks

    • C

      Caltech

      Pricing Options with Mathematical Models

      Skills you'll gain: Derivatives, Financial Market, Risk Modeling, Mathematical Modeling, Financial Modeling, Credit Risk, Risk Management, Portfolio Management, Probability, Differential Equations, Applied Mathematics, Probability Distribution, Calculus

      4.7
      Rating, 4.7 out of 5 stars
      ·
      36 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      D

      DeepLearning.AI

      Applied Statistics for Data Analytics

      Skills you'll gain: Probability & Statistics, Statistical Analysis, Statistics, Statistical Modeling, Statistical Hypothesis Testing, Statistical Visualization, Descriptive Statistics, Data Analysis, Histogram, Probability, Probability Distribution, Correlation Analysis, Statistical Inference, Estimation, Simulation and Simulation Software, Sampling (Statistics), Analytical Skills, Spreadsheet Software, Generative AI

      4.8
      Rating, 4.8 out of 5 stars
      ·
      17 reviews

      Beginner · Course · 1 - 4 Weeks

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

    • Estatística não-paramétrica para a tomada de decisão: Universidade de São Paulo
    • Build Decision Trees, SVMs, and Artificial Neural Networks: CertNexus
    • Financial Risk Management with R: Duke University
    • Statistics for International Business: University of London
    • Aléatoire : une introduction aux probabilités - Partie 1: École Polytechnique
    • 医学统计学与SPSS软件(基础篇): Peking University
    • Applied Machine Learning: Johns Hopkins University
    • Classification Analysis: University of Colorado Boulder
    • Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital: Duke University
    • Random Processes: Johns Hopkins 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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