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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
      Free
      S

      Stanford University

      Social and Economic Networks: Models and Analysis

      Skills you'll gain: Network Analysis, Network Model, Social Sciences, Sociology, Economics, Policy, and Social Studies, Game Theory, Behavioral Economics, Graph Theory, Mathematical Modeling, Markov Model, Probability & Statistics, Probability Distribution, Bayesian Statistics, Simulations

      4.8
      Rating, 4.8 out of 5 stars
      ·
      754 reviews

      Advanced · Course · 1 - 3 Months

    • T

      The University of Chicago

      Machine Learning: Concepts and Applications

      Skills you'll gain: Unsupervised Learning, Machine Learning Algorithms, Deep Learning, Machine Learning, Classification And Regression Tree (CART), Decision Tree Learning, Applied Machine Learning, Scikit Learn (Machine Learning Library), Supervised Learning, Regression Analysis, Random Forest Algorithm, Dimensionality Reduction, Statistical Methods, Tensorflow, Feature Engineering, Artificial Neural Networks, Pandas (Python Package)

      3.9
      Rating, 3.9 out of 5 stars
      ·
      22 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Support Vector Machines in Python, From Start to Finish

      Skills you'll gain: Scikit Learn (Machine Learning Library), Tensorflow, Cloud Computing, Classification And Regression Tree (CART), Supervised Learning, Applied Machine Learning, Machine Learning Methods, Pandas (Python Package), Data Visualization, Data Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      155 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • U

      University of Pennsylvania

      Modeling Risk and Realities

      Skills you'll gain: Risk Modeling, Probability Distribution, Mathematical Modeling, Statistical Modeling, Risk Management, Data Visualization, Predictive Modeling, Data Modeling, Probability & Statistics, Risk Analysis, Simulation and Simulation Software, Forecasting, Data-Driven Decision-Making, Business Analysis, Process Optimization, Microsoft Excel

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.2K reviews

      Mixed · Course · 1 - 4 Weeks

    • N

      New York Institute of Finance

      Introduction to Risk Management

      Skills you'll gain: Risk Management, Business Risk Management, Risk Modeling, Operational Risk, Enterprise Risk Management (ERM), Credit Risk, Risk Analysis, Portfolio Management, Capital Markets, Financial Market, Financial Regulation, Financial Modeling, Probability Distribution

      4.6
      Rating, 4.6 out of 5 stars
      ·
      686 reviews

      Beginner · Course · 1 - 3 Months

    • 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 Colorado Boulder

      Probability Theory: Foundation for Data Science

      Skills you'll gain: Probability, Probability & Statistics, Probability Distribution, Statistics, Bayesian Statistics, Data Science, Statistical Analysis, Statistical Inference

      Build toward a degree

      4.5
      Rating, 4.5 out of 5 stars
      ·
      253 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Data Analysis Using Pyspark

      Skills you'll gain: PySpark, Matplotlib, Apache Spark, Big Data, Data Processing, Distributed Computing, Data Visualization, Data Analysis, Data Manipulation, Query Languages, Google Cloud Platform

      4.5
      Rating, 4.5 out of 5 stars
      ·
      301 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free
      Free
      U

      University of Amsterdam

      Data Analytics for Lean Six Sigma

      Skills you'll gain: Lean Six Sigma, Statistical Hypothesis Testing, Minitab, Regression Analysis, Data Visualization Software, Probability Distribution, Descriptive Statistics, Data Analysis, Statistical Analysis, Box Plots, Analytics, Process Improvement, Correlation Analysis, Variance Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.4K reviews

      Beginner · Course · 1 - 3 Months

    • S

      Stanford University

      Probabilistic Graphical Models 1: Representation

      Skills you'll gain: Bayesian Network, Graph Theory, Probability Distribution, Statistical Modeling, Markov Model, Decision Support Systems, Probability & Statistics, Network Analysis, Applied Machine Learning, Natural Language Processing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.4K reviews

      Advanced · Course · 1 - 3 Months

    • U

      University of Washington

      Machine Learning: Clustering & Retrieval

      Skills you'll gain: Unsupervised Learning, Bayesian Statistics, Applied Machine Learning, Data Mining, Statistical Machine Learning, Big Data, Statistical Inference, Text Mining, Statistical Modeling, Machine Learning Algorithms, Unstructured Data, Machine Learning, Sampling (Statistics), Scalability, Probability Distribution, Algorithms

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.4K reviews

      Mixed · Course · 1 - 3 Months

    • U

      University of Michigan

      Data Science Ethics

      Skills you'll gain: Data Ethics, Data Sharing, Information Privacy, General Data Protection Regulation (GDPR), Personally Identifiable Information, Data Security, Data Governance, Ethical Standards And Conduct, Big Data, Intellectual Property, Data Analysis, Social Sciences, Sampling (Statistics), Data-Driven Decision-Making, Diversity Awareness

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.2K reviews

      Beginner · Course · 1 - 3 Months

    Random Forest learners also search

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    1…91011…33

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

    • Social and Economic Networks: Models and Analysis: Stanford University
    • Machine Learning: Concepts and Applications: The University of Chicago
    • Support Vector Machines in Python, From Start to Finish: Coursera Project Network
    • Modeling Risk and Realities: University of Pennsylvania
    • Introduction to Risk Management: New York Institute of Finance
    • Bayesian Statistics: Duke University
    • Probability Theory: Foundation for Data Science: University of Colorado Boulder
    • Data Analysis Using Pyspark: Coursera Project Network
    • Data Analytics for Lean Six Sigma: University of Amsterdam
    • Probabilistic Graphical Models 1: Representation: Stanford 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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