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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
      P

      Packt

      Deep Learning: Recurrent Neural Networks with Python

      Skills you'll gain: PyTorch (Machine Learning Library), Tensorflow, Artificial Intelligence, Applied Machine Learning, Artificial Neural Networks, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Application Deployment, Text Mining, Machine Learning, Natural Language Processing, Predictive Modeling, Python Programming, Time Series Analysis and Forecasting, Network Architecture, Machine Learning Algorithms, Data Processing, Data Analysis

      Beginner · Specialization · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Hyperparameter Tuning with Keras Tuner

      Skills you'll gain: Keras (Neural Network Library), Artificial Neural Networks, Applied Machine Learning, Deep Learning, Python Programming, Performance Tuning, Machine Learning Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      69 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • N

      National Taiwan University

      機器學習技法 (Machine Learning Techniques)

      Skills you'll gain: Feature Engineering, Classification And Regression Tree (CART), Statistical Machine Learning, Supervised Learning, Machine Learning Algorithms, Random Forest Algorithm, Deep Learning, Machine Learning, Applied Machine Learning, Artificial Neural Networks, Dimensionality Reduction, Regression Analysis

      4.9
      Rating, 4.9 out of 5 stars
      ·
      35 reviews

      Intermediate · Course · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Measurement – Turning Concepts into Data

      Skills you'll gain: Surveys, Survey Creation, Sampling (Statistics), Quantitative Research, Research Methodologies, Data Analysis, Data Quality, Data Transformation, Data Modeling, Data Validation, Statistical Methods

      4.7
      Rating, 4.7 out of 5 stars
      ·
      77 reviews

      Beginner · Course · 1 - 4 Weeks

    • É

      École Polytechnique

      Aléatoire : une introduction aux probabilités - Partie 2

      Skills you'll gain: Probability, Probability Distribution, Probability & Statistics, Advanced Mathematics, Mathematical Theory & Analysis, Simulations, Statistical Methods, Statistical Inference

      4.6
      Rating, 4.6 out of 5 stars
      ·
      39 reviews

      Mixed · Course · 1 - 3 Months

    • C

      Coursera Project Network

      RStudio for Six Sigma - Basic Descriptive Statistics

      Skills you'll gain: Sampling (Statistics), Statistical Methods, Descriptive Statistics, Data Visualization, Data Import/Export, Pareto Chart, Histogram, Statistical Analysis, Six Sigma Methodology, Box Plots, R Programming, Probability Distribution, Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      64 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Statistical Methods for Computer Science

      Skills you'll gain: Network Analysis, R Programming, Statistical Analysis, Regression Analysis, Statistical Modeling, Statistical Methods, Combinatorics, Bayesian Network, Statistical Hypothesis Testing, Data Analysis, Probability, Probability & Statistics, Bayesian Statistics, Probability Distribution, Simulations, Data Science, Markov Model, Applied Mathematics, Graph Theory, Statistics

      Intermediate · Specialization · 3 - 6 Months

    • E

      EIT Digital

      Approximation Algorithms

      Skills you'll gain: Algorithms, Graph Theory, Computational Thinking, Applied Mathematics, Theoretical Computer Science, Linear Algebra

      4.7
      Rating, 4.7 out of 5 stars
      ·
      33 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Pennsylvania

      Statistics for Data Science Essentials

      Skills you'll gain: Probability, Probability & Statistics, Sampling (Statistics), Probability Distribution, Statistics, Data Science, Statistical Inference, Descriptive Statistics, Statistical Analysis, General Mathematics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7 reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of Colorado Boulder

      Introduction to Computer Vision

      Skills you'll gain: Image Analysis, Computer Vision, Computer Graphics, Deep Learning, Data Ethics, Computational Thinking, Artificial Intelligence, Data Processing, Linear Algebra, Probability Distribution

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      9 reviews

      Beginner · Course · 1 - 4 Weeks

    • E

      EIT Digital

      Geometric Algorithms

      Skills you'll gain: Geometry, Algorithms, Computer Graphics, Data Structures, Graph Theory, Computational Thinking, Geographic Information Systems, Virtual Reality, Theoretical Computer Science, Spatial Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      24 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Managing, Describing, and Analyzing Data

      Skills you'll gain: Statistical Inference, Probability Distribution, Statistical Analysis, Descriptive Statistics, Sampling (Statistics), Statistics, Probability & Statistics, Statistical Hypothesis Testing, R Programming, Data Analysis, Data Literacy, Probability, Statistical Visualization, Histogram

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      33 reviews

      Beginner · Course · 1 - 3 Months

    Random Forest learners also search

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

    • Deep Learning: Recurrent Neural Networks with Python: Packt
    • Hyperparameter Tuning with Keras Tuner: Coursera Project Network
    • 機器學習技法 (Machine Learning Techniques): National Taiwan University
    • Measurement – Turning Concepts into Data: Johns Hopkins University
    • Aléatoire : une introduction aux probabilités - Partie 2: École Polytechnique
    • RStudio for Six Sigma - Basic Descriptive Statistics: Coursera Project Network
    • Statistical Methods for Computer Science: Johns Hopkins University
    • Approximation Algorithms: EIT Digital
    • Statistics for Data Science Essentials: University of Pennsylvania
    • Introduction to Computer Vision: University of Colorado Boulder

    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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