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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 Colorado Boulder

      Fundamentals of Macroscopic and Microscopic Thermodynamics

      Skills you'll gain: Statistical Methods, Physics, Probability Distribution, Physical Science, Chemistry, Engineering Calculations, Calculus

      4.3
      Rating, 4.3 out of 5 stars
      ·
      262 reviews

      Advanced · Course · 1 - 4 Weeks

    • U

      University of Minnesota

      Introduction to Automated Analysis

      Skills you'll gain: Test Automation, Regression Testing, Software Testing, Unit Testing, Test Tools, System Testing, Code Coverage, Security Testing, Verification And Validation, Test Case, Test Data, Debugging, Automation, Functional Requirement, Application Security

      4.2
      Rating, 4.2 out of 5 stars
      ·
      82 reviews

      Intermediate · Course · 1 - 4 Weeks

    • C

      CertNexus

      Build Regression, Classification, and Clustering Models

      Skills you'll gain: Unsupervised Learning, Regression Analysis, Machine Learning Algorithms, Linear Algebra, Machine Learning, Predictive Modeling, Statistical Methods, Supervised Learning, Feature Engineering, Classification And Regression Tree (CART), Performance Tuning, Algorithms

      4.4
      Rating, 4.4 out of 5 stars
      ·
      16 reviews

      Intermediate · Course · 1 - 3 Months

    • M

      Macquarie University

      Create video, audio and infographics for online learning

      Skills you'll gain: Video Production, Infographics, Multimedia, Peer Review, Content Creation, Constructive Feedback, Design Thinking, Media Production, Design, Storytelling, Scripting

      4.8
      Rating, 4.8 out of 5 stars
      ·
      97 reviews

      Beginner · Course · 1 - 3 Months

    • A

      Arizona State University

      Introduction to Machine Learning with Python

      Skills you'll gain: Supervised Learning, Unsupervised Learning, Generative AI, Deep Learning, Image Analysis, Machine Learning Algorithms, Applied Machine Learning, Python Programming, Machine Learning, Artificial Neural Networks, Computer Vision, Computer Programming, Regression Analysis

      3.7
      Rating, 3.7 out of 5 stars
      ·
      20 reviews

      Beginner · Course · 1 - 4 Weeks

    • 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

    • U

      University of Colorado Boulder

      Statistical Inference and Hypothesis Testing in Data Science Applications

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Data Ethics, Probability & Statistics, Statistical Inference, Statistical Analysis, Quantitative Research, Statistics, Probability Distribution

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      50 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Colorado Boulder

      Data Analysis with Python Project

      Skills you'll gain: Dimensionality Reduction, Data Analysis, Supervised Learning, Anomaly Detection, Machine Learning, Machine Learning Algorithms, Statistical Analysis, Unsupervised Learning, Data Mining, Analytics, Predictive Modeling, Regression Analysis, Scikit Learn (Machine Learning Library), Classification And Regression Tree (CART), Exploratory Data Analysis, Statistical Methods

      5
      Rating, 5 out of 5 stars
      ·
      6 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Diabetes Prediction With Pyspark MLLIB

      Skills you'll gain: Data Cleansing, Apache Spark, PySpark, Data Manipulation, Applied Machine Learning, Data Processing, Classification And Regression Tree (CART), Predictive Modeling, Regression Analysis, Machine Learning, Google Cloud Platform

      4.6
      Rating, 4.6 out of 5 stars
      ·
      22 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • J

      Johns Hopkins University

      Tidyverse Skills for Data Science in R

      Skills you'll gain: Tidyverse (R Package), Ggplot2, Data Wrangling, Statistical Modeling, Exploratory Data Analysis, Plot (Graphics), R Programming, Data Import/Export, Predictive Modeling, Statistical Visualization, Sampling (Statistics), Data Visualization Software, Statistical Hypothesis Testing, Data Analysis, Data Manipulation, Data Modeling, Web Scraping, Data Integration, Data Cleansing, Data Transformation

      4.5
      Rating, 4.5 out of 5 stars
      ·
      104 reviews

      Beginner · Specialization · 3 - 6 Months

    • 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

    • J

      Johns Hopkins University

      Mathematical Biostatistics Boot Camp 2

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Analysis, Statistical Methods, Probability & Statistics, Statistical Inference, Biostatistics, Sampling (Statistics), Data Analysis, Sample Size Determination, Risk Analysis, Probability Distribution

      4.3
      Rating, 4.3 out of 5 stars
      ·
      133 reviews

      Mixed · Course · 1 - 4 Weeks

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

    • Fundamentals of Macroscopic and Microscopic Thermodynamics: University of Colorado Boulder
    • Introduction to Automated Analysis: University of Minnesota
    • Build Regression, Classification, and Clustering Models: CertNexus
    • Create video, audio and infographics for online learning : Macquarie University
    • Introduction to Machine Learning with Python: Arizona State University
    • Statistical Inference for Estimation in Data Science: University of Colorado Boulder
    • Statistical Inference and Hypothesis Testing in Data Science Applications: University of Colorado Boulder
    • Data Analysis with Python Project : University of Colorado Boulder
    • Diabetes Prediction With Pyspark MLLIB: Coursera Project Network
    • Tidyverse Skills for Data Science in R: 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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