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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

    • Status: Free Trial
      Free Trial
      É

      École Polytechnique Fédérale de Lausanne

      Functional Program Design in Scala

      Skills you'll gain: Scala Programming, Software Design, Software Design Patterns, Functional Design, Event-Driven Programming, Java, Programming Principles, Performance Tuning, Data Structures, Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      A

      Arizona State University

      Design of Experiments

      Skills you'll gain: Experimentation, Sample Size Determination, Research Design, Regression Analysis, Statistical Analysis, Statistical Methods, Data Analysis Software, Statistical Modeling, Design Strategies, Probability & Statistics, Data Analysis, Mathematical Modeling, Data Transformation, Descriptive Statistics, Probability Distribution, Statistical Hypothesis Testing, Variance Analysis, Quality Control

      4.7
      Rating, 4.7 out of 5 stars
      ·
      360 reviews

      Beginner · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Principal Component Analysis with NumPy

      Skills you'll gain: Exploratory Data Analysis, NumPy, Data Visualization, Data Analysis, Seaborn, Matplotlib, Cloud Computing, Jupyter, Dimensionality Reduction, Unsupervised Learning, Applied Machine Learning, Python Programming, Linear Algebra

      4.6
      Rating, 4.6 out of 5 stars
      ·
      295 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Classification Trees in Python, From Start To Finish

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Data Transformation, Supervised Learning, Predictive Modeling, Feature Engineering, Scikit Learn (Machine Learning Library), Data Processing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      230 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      U

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Skills you'll gain: Bayesian Statistics, Statistical Modeling, Statistical Methods, Markov Model, Statistical Analysis, Regression Analysis, R Programming, Simulations, Statistical Inference, Data Analysis, Probability, Probability Distribution

      4.8
      Rating, 4.8 out of 5 stars
      ·
      491 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      R

      Rice University

      Business Applications of Hypothesis Testing and Confidence Interval Estimation

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Sample Size Determination, Statistical Inference, Estimation, Statistics, Probability & Statistics, Sampling (Statistics), Statistical Analysis, Microsoft Excel, Excel Formulas, Decision Making

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      R

      Rice University

      Portfolio Selection and Risk Management

      Skills you'll gain: Portfolio Management, Investment Management, Investments, Financial Market, Risk Management, Equities, Finance, Return On Investment, Risk Analysis, Statistical Methods, Probability Distribution, Correlation Analysis, Decision Making, Quantitative Research, Variance Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      609 reviews

      Mixed · Course · 1 - 3 Months

    • U

      Universidad Austral

      Estadística aplicada a los negocios

      Skills you'll gain: Regression Analysis, Statistical Inference, Descriptive Statistics, Risk Analysis, Business Risk Management, Business Analytics, Statistics, Sampling (Statistics), Microsoft Excel, Data Analysis, Probability, Statistical Analysis, Data-Driven Decision-Making, Probability Distribution, Statistical Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      756 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      S

      Stanford University

      Probabilistic Graphical Models 2: Inference

      Skills you'll gain: Bayesian Network, Bayesian Statistics, Statistical Inference, Markov Model, Graph Theory, Sampling (Statistics), Applied Machine Learning, Statistical Methods, Probability & Statistics, Algorithms, Probability Distribution, Machine Learning Algorithms, Computational Thinking

      4.6
      Rating, 4.6 out of 5 stars
      ·
      488 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Hypothesis Testing in Public Health

      Skills you'll gain: Statistical Hypothesis Testing, Biostatistics, Sampling (Statistics), Statistical Inference, Scientific Methods, Statistical Analysis, Quantitative Research, Medical Science and Research, Probability & Statistics, Public Health

      4.8
      Rating, 4.8 out of 5 stars
      ·
      637 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      C

      CertNexus

      CertNexus Certified Artificial Intelligence Practitioner

      Skills you'll gain: Data Ethics, Unsupervised Learning, Random Forest Algorithm, Data Analysis, Regression Analysis, Decision Tree Learning, Machine Learning Algorithms, Data Collection, Deep Learning, Workflow Management, MLOps (Machine Learning Operations), Statistical Analysis, Linear Algebra, Applied Machine Learning, Business Ethics, Compliance Management, Learning Strategies, Test Planning, Productivity, Registration

      4.6
      Rating, 4.6 out of 5 stars
      ·
      271 reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Fitting Statistical Models to Data with Python

      Skills you'll gain: Statistical Modeling, Statistical Methods, Bayesian Statistics, Statistical Inference, Statistical Analysis, Statistical Programming, Regression Analysis, Predictive Modeling, Jupyter, Exploratory Data Analysis, Statistical Hypothesis Testing, Correlation Analysis, Probability Distribution

      4.4
      Rating, 4.4 out of 5 stars
      ·
      702 reviews

      Intermediate · Course · 1 - 4 Weeks

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

    • Functional Program Design in Scala: École Polytechnique Fédérale de Lausanne
    • Design of Experiments: Arizona State University
    • Principal Component Analysis with NumPy: Coursera Project Network
    • Classification Trees in Python, From Start To Finish: Coursera Project Network
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Business Applications of Hypothesis Testing and Confidence Interval Estimation : Rice University
    • Portfolio Selection and Risk Management: Rice University
    • Estadística aplicada a los negocios: Universidad Austral
    • Probabilistic Graphical Models 2: Inference: Stanford University
    • Hypothesis Testing in Public Health : 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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