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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
      U

      University of Michigan

      Sampling People, Networks and Records

      Skills you'll gain: Sampling (Statistics), Sample Size Determination, Data Collection, Surveys, Quantitative Research, Statistical Methods, Statistical Software, Probability & Statistics, Probability, Research Methodologies, Network Analysis

      4.3
      Rating, 4.3 out of 5 stars
      ·
      104 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      O

      O.P. Jindal Global University

      Supervised Learning and Its Applications in Marketing

      Skills you'll gain: Marketing Analytics, Supervised Learning, Customer Retention, Applied Machine Learning, Predictive Analytics, Scikit Learn (Machine Learning Library), Marketing Strategies, Customer Insights, Machine Learning, Python Programming, Regression Analysis, Personalized Service, Artificial Intelligence and Machine Learning (AI/ML), Application Deployment

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      C

      Columbia University

      Computational Methods in Pricing and Model Calibration

      Skills you'll gain: Regression Analysis, Derivatives, Financial Market, Statistical Methods, Financial Modeling, Securities (Finance), Mathematical Modeling, Numerical Analysis, Equities, Financial Data, Python Programming, Probability Distribution, Algorithms

      4.4
      Rating, 4.4 out of 5 stars
      ·
      40 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      C

      Columbia University

      Advanced Topics in Derivative Pricing

      Skills you'll gain: Derivatives, Credit Risk, Financial Market, Capital Markets, Futures Exchange, Equities, Risk Analysis, Risk Management, Market Dynamics, Portfolio Management, Financial Modeling, Mathematical Modeling, Probability Distribution

      4.4
      Rating, 4.4 out of 5 stars
      ·
      30 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Statistical Inference for Estimation in Data Science

      Skills you'll gain: Statistical Inference, Statistical Methods, Probability & Statistics, Statistical Modeling, Sampling (Statistics), Statistical Analysis, Data Science, Probability Distribution

      4.1
      Rating, 4.1 out of 5 stars
      ·
      88 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      L

      LearnQuest

      Advanced AI Techniques for the Supply Chain

      Skills you'll gain: Image Analysis, Supervised Learning, Applied Machine Learning, Predictive Modeling, Anomaly Detection, Statistical Modeling, Supply Chain Management, Machine Learning, Computer Vision, Supply Chain, Deep Learning, Classification And Regression Tree (CART), Random Forest Algorithm, Natural Language Processing, Artificial Neural Networks, Customer Demand Planning, Forecasting, Unsupervised Learning, Performance Tuning

      3.4
      Rating, 3.4 out of 5 stars
      ·
      14 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Practical Predictive Analytics: Models and Methods

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Statistical Machine Learning, Predictive Analytics, Advanced Analytics, Statistical Methods, Decision Tree Learning, Statistical Inference, Statistical Analysis, Machine Learning Algorithms, Machine Learning, Graph Theory, Probability & Statistics, Big Data

      4.1
      Rating, 4.1 out of 5 stars
      ·
      320 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Advanced Linear Models for Data Science 2: Statistical Linear Models

      Skills you'll gain: Regression Analysis, Linear Algebra, R Programming, Probability Distribution, Statistical Modeling, Mathematical Modeling, Probability & Statistics, Applied Mathematics, Statistical Analysis, Integral Calculus

      4.6
      Rating, 4.6 out of 5 stars
      ·
      101 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      P

      Packt

      R Ultimate 2023 - R for Data Science and Machine Learning

      Skills you'll gain: Rmarkdown, Deep Learning, Shiny (R Package), Data Import/Export, Regression Analysis, Dimensionality Reduction, R Programming, Data Manipulation, Data Visualization, Reinforcement Learning, Web Scraping, Ggplot2, Plotly, Applied Machine Learning, Image Analysis, Artificial Intelligence, Data Mining, Machine Learning, PyTorch (Machine Learning Library), Predictive Modeling

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Maryland, College Park

      Dealing With Missing Data

      Skills you'll gain: Sampling (Statistics), Statistical Programming, Data Cleansing, Data Quality, Data Analysis Software, Statistical Analysis, Statistical Methods, Statistical Modeling, R Programming, Regression Analysis, Statistical Inference

      3.8
      Rating, 3.8 out of 5 stars
      ·
      135 reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Evaluating Large-Scale Health Programs

      Skills you'll gain: Surveys, Program Evaluation, Survey Creation, Health Policy, Sampling (Statistics), Health Systems, Health Assessment, Systems Thinking, Health Equity, Data Collection, Quantitative Research, Training Programs, Data Analysis, Maternal Health, Data Management, Statistical Analysis, Analysis, Nutrition and Diet, Public Health, Research Design

      4.5
      Rating, 4.5 out of 5 stars
      ·
      65 reviews

      Intermediate · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Decision Tree Classifier for Beginners in R

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Predictive Modeling, Data Manipulation, Statistical Modeling, R Programming, Supervised Learning, Machine Learning Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6 reviews

      Beginner · Guided Project · Less Than 2 Hours

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

    • Sampling People, Networks and Records: University of Michigan
    • Supervised Learning and Its Applications in Marketing: O.P. Jindal Global University
    • Computational Methods in Pricing and Model Calibration: Columbia University
    • Advanced Topics in Derivative Pricing: Columbia University
    • Statistical Inference for Estimation in Data Science: University of Colorado Boulder
    • Advanced AI Techniques for the Supply Chain: LearnQuest
    • Practical Predictive Analytics: Models and Methods: University of Washington
    • Advanced Linear Models for Data Science 2: Statistical Linear Models: Johns Hopkins University
    • R Ultimate 2023 - R for Data Science and Machine Learning: Packt
    • Dealing With Missing Data: University of Maryland, College Park

    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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