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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

    • D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, Statistical Hypothesis Testing, Statistical Modeling, Statistical Methods, Statistical Inference, Statistical Analysis, Regression Analysis, Data Analysis, R Programming, Probability, Data-Driven Decision-Making, Probability Distribution

      3.8
      Rating, 3.8 out of 5 stars
      ·
      797 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Alberta

      Prediction and Control with Function Approximation

      Skills you'll gain: Reinforcement Learning, Deep Learning, Feature Engineering, Machine Learning, Supervised Learning, Artificial Neural Networks, Pseudocode, Linear Algebra, Probability Distribution

      4.8
      Rating, 4.8 out of 5 stars
      ·
      834 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Amsterdam

      Inferential Statistics

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Regression Analysis, Statistical Inference, Statistical Analysis, Quantitative Research, Statistical Modeling, Probability Distribution, R Programming

      4.3
      Rating, 4.3 out of 5 stars
      ·
      597 reviews

      Mixed · Course · 1 - 3 Months

    • 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

    • 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

    • C

      Coursera Project Network

      Scikit-Learn For Machine Learning Classification Problems

      Skills you'll gain: Scikit Learn (Machine Learning Library), Applied Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Supervised Learning, Random Forest Algorithm, Unsupervised Learning

      4.5
      Rating, 4.5 out of 5 stars
      ·
      13 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • L

      LearnQuest

      Neural Networks and Random Forests

      Skills you'll gain: Random Forest Algorithm, Keras (Neural Network Library), Classification And Regression Tree (CART), Tensorflow, Deep Learning, Artificial Neural Networks, Predictive Modeling, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning Algorithms, Regression Analysis, Machine Learning, Python Programming, Network Architecture

      2.9
      Rating, 2.9 out of 5 stars
      ·
      13 reviews

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Machine Learning with PySpark: Customer Churn Analysis

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Analysis, PySpark, Data Processing, Data Cleansing, Data Transformation, Apache Spark, Data-Driven Decision-Making, Decision Tree Learning, Predictive Modeling, Predictive Analytics, Applied Machine Learning, Application Deployment, Machine Learning

      4.8
      Rating, 4.8 out of 5 stars
      ·
      18 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • G

      Google

      Google Data Analytics

      Skills you'll gain: Data Presentation, Data Storytelling, Presentations, Data Cleansing, Data Visualization, Rmarkdown, Data-Driven Decision-Making, Data Validation, Data Ethics, Analytical Skills, Dashboard, Spreadsheet Software, Ggplot2, SQL, Data Visualization Software, Data Literacy, Data Collection, Sampling (Statistics), Data Analysis, Data Processing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      312 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of Michigan

      Introduction to Machine Learning in Sports Analytics

      Skills you'll gain: Scikit Learn (Machine Learning Library), Supervised Learning, Applied Machine Learning, Statistical Machine Learning, Predictive Analytics, Feature Engineering, Classification And Regression Tree (CART), Machine Learning Algorithms, Predictive Modeling, Analytics, Machine Learning, Data Analysis, Random Forest Algorithm

      4.8
      Rating, 4.8 out of 5 stars
      ·
      24 reviews

      Intermediate · Course · 1 - 4 Weeks

    • S

      Stanford University

      Probabilistic Graphical Models 3: Learning

      Skills you'll gain: Bayesian Network, Applied Machine Learning, Machine Learning Algorithms, Markov Model, Machine Learning, Statistical Modeling, Network Analysis, Probability Distribution, Statistical Methods, Probability & Statistics, Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      303 reviews

      Advanced · Course · 1 - 3 Months

    • U

      University of Minnesota

      Analytics for Decision Making

      Skills you'll gain: Time Series Analysis and Forecasting, Simulations, Operations Research, Probability Distribution, Mathematical Modeling, Supply Chain, Probability, Predictive Modeling, Business Modeling, Business Analytics, Analytics, Regression Analysis, Microsoft Excel, Forecasting, Data Modeling, Process Optimization, Data-Driven Decision-Making, Statistics, Business Mathematics, Manufacturing Operations

      4.7
      Rating, 4.7 out of 5 stars
      ·
      259 reviews

      Beginner · Specialization · 3 - 6 Months

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    1…101112…33

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

    • Bayesian Statistics: Duke University
    • Prediction and Control with Function Approximation: University of Alberta
    • Inferential Statistics: University of Amsterdam
    • Business Applications of Hypothesis Testing and Confidence Interval Estimation : Rice University
    • Classification Trees in Python, From Start To Finish: Coursera Project Network
    • Scikit-Learn For Machine Learning Classification Problems: Coursera Project Network
    • Neural Networks and Random Forests: LearnQuest
    • Machine Learning with PySpark: Customer Churn Analysis: Coursera Project Network
    • Google Data Analytics: Google
    • Introduction to Machine Learning in Sports Analytics: University of Michigan

    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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