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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: New
      New
      Status: Free Trial
      Free Trial
      E

      Edureka

      Predictive Modeling with Python

      Skills you'll gain: Probability Distribution, Predictive Modeling, Exploratory Data Analysis, Statistical Inference, Data Analysis, Statistical Analysis, Probability & Statistics, Statistical Hypothesis Testing, Descriptive Statistics, Data Cleansing, Data Validation, Regression Analysis, Feature Engineering, Data Processing, Machine Learning

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Using probability distributions for real world problems in R

      Skills you'll gain: Statistical Inference, Probability Distribution, R Programming, Statistical Visualization, Statistics, Statistical Analysis, Statistical Hypothesis Testing, Data Analysis, Data Science, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      29 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      I

      Interactive Brokers

      ESG Investing: Industry Impacts & Transformations

      Skills you'll gain: Environmental Social And Corporate Governance (ESG), Financial Market, Market Trend, Market Analysis, Corporate Sustainability, Governance, Investment Management, Transportation Operations, Portfolio Management, Credit Risk, Business Transformation, Supply And Demand, Emerging Technologies

      4.7
      Rating, 4.7 out of 5 stars
      ·
      25 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      P

      Packt

      RNN Architecture and Sentiment Classification

      Skills you'll gain: PyTorch (Machine Learning Library), Text Mining, Artificial Neural Networks, Natural Language Processing, Deep Learning, Applied Machine Learning, Network Architecture, Time Series Analysis and Forecasting, Machine Learning Algorithms, Data Processing

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      P

      Packt

      Foundations of ML & Python for Data Science

      Skills you'll gain: Data Manipulation, Python Programming, Probability & Statistics, Pandas (Python Package), Data Science, Statistical Analysis, NumPy, Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Descriptive Statistics, Artificial Intelligence and Machine Learning (AI/ML), Data Analysis, Probability

      Beginner · Course · 1 - 4 Weeks

    • U

      Universidad Nacional Autónoma de México

      Estadística y probabilidad: principios de Inferencia

      Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability Distribution, Sampling (Statistics), Probability & Statistics, Probability, Statistics, Statistical Analysis, Descriptive Statistics

      2.9
      Rating, 2.9 out of 5 stars
      ·
      15 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Statistical Learning for Data Science

      Skills you'll gain: Statistical Modeling, Applied Machine Learning, Unsupervised Learning, Statistical Machine Learning, Classification And Regression Tree (CART), Data Science, Decision Tree Learning, Statistical Methods, Artificial Neural Networks, Statistical Analysis, Regression Analysis, R Programming, Predictive Modeling, Supervised Learning, Statistical Inference, Advanced Analytics, Dimensionality Reduction, Random Forest Algorithm, Machine Learning, Sampling (Statistics)

      Build toward a degree

      4
      Rating, 4 out of 5 stars
      ·
      17 reviews

      Intermediate · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      MLOps in R: Deploying machine learning models using vetiver

      Skills you'll gain: MLOps (Machine Learning Operations), Continuous Deployment, Application Deployment, Tidyverse (R Package), R Programming, Dashboard, Applied Machine Learning, Health Informatics, Continuous Monitoring, Predictive Modeling, Machine Learning Methods, Statistical Machine Learning, Docker (Software), Application Programming Interface (API)

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Interpretable Machine Learning Applications: Part 2

      Skills you'll gain: Classification And Regression Tree (CART), Applied Machine Learning, Random Forest Algorithm, Data Processing, Machine Learning Algorithms, Machine Learning Methods, Predictive Modeling, Regression Analysis, Feature Engineering, Machine Learning, Exploratory Data Analysis, Performance Measurement

      4.2
      Rating, 4.2 out of 5 stars
      ·
      22 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • P

      Packt

      Deep Learning - Recurrent Neural Networks with TensorFlow

      Skills you'll gain: Time Series Analysis and Forecasting, Tensorflow, Natural Language Processing, Image Analysis, Deep Learning, Text Mining, Artificial Neural Networks, Predictive Analytics

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Statistics and Data Analysis with Excel, Part 2

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Analysis, Sampling (Statistics), Statistical Methods, Microsoft Excel, Probability & Statistics, Data Analysis, Regression Analysis, Probability Distribution

      5
      Rating, 5 out of 5 stars
      ·
      7 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      What are the Chances? Probability and Uncertainty in Statistics

      Skills you'll gain: Statistics, Regression Analysis, Probability, Statistical Hypothesis Testing, Probability Distribution, Statistical Analysis, Statistical Inference, Sampling (Statistics), Combinatorics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      17 reviews

      Intermediate · Course · 1 - 4 Weeks

    Random Forest learners also search

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

    • Predictive Modeling with Python : Edureka
    • Using probability distributions for real world problems in R: Coursera Project Network
    • ESG Investing: Industry Impacts & Transformations: Interactive Brokers
    • RNN Architecture and Sentiment Classification: Packt
    • Foundations of ML & Python for Data Science: Packt
    • Estadística y probabilidad: principios de Inferencia: Universidad Nacional Autónoma de México
    • Statistical Learning for Data Science: University of Colorado Boulder
    • MLOps in R: Deploying machine learning models using vetiver: Coursera Project Network
    • Interpretable Machine Learning Applications: Part 2: Coursera Project Network
    • Deep Learning - Recurrent Neural Networks with TensorFlow: Packt

    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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