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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
      D

      DeepLearning.AI

      How Diffusion Models Work

      Skills you'll gain: Generative AI, Jupyter, PyTorch (Machine Learning Library), Image Analysis, Sampling (Statistics), Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      260 reviews

      Intermediate · Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Statistics For Data Science

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

      3.9
      Rating, 3.9 out of 5 stars
      ·
      33 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      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

      ML Parameters Optimization: GridSearch, Bayesian, Random

      Skills you'll gain: Scikit Learn (Machine Learning Library), Regression Analysis, Performance Tuning, Applied Machine Learning, Machine Learning Methods, Statistical Machine Learning, Bayesian Statistics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      7 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • P

      Packt

      No-Code Machine Learning Using Amazon AWS SageMaker Canvas

      Skills you'll gain: AWS SageMaker, Applied Machine Learning, Machine Learning, Amazon Web Services, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Data Processing, Version Control, Amazon S3, Data Validation, Cloud Services

      Beginner · Course · 1 - 3 Months

    • Status: New
      New
      J

      Johns Hopkins University

      Introduction to AI: Key Concepts and Applications

      Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Performance Metric, Strategic Leadership, Strategic Decision-Making, Data Quality, Applied Machine Learning, Data Ethics, Machine Learning, Supervised Learning, Algorithms, Artificial Neural Networks, Data Validation, Random Forest Algorithm, Resource Utilization, System Requirements

      4.8
      Rating, 4.8 out of 5 stars
      ·
      17 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      P

      Packt

      Deep Learning with Real-World Projects

      Skills you'll gain: Matplotlib, Data Visualization, Deep Learning, Linear Algebra, Artificial Neural Networks, NumPy, Image Analysis, Keras (Neural Network Library), Seaborn, Pandas (Python Package), Tensorflow, Machine Learning, Applied Machine Learning, Computer Vision, Scikit Learn (Machine Learning Library), Supervised Learning, Python Programming, Jupyter, Machine Learning Methods, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      6 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      M

      MathWorks

      Predictive Modeling and Machine Learning with MATLAB

      Skills you'll gain: Supervised Learning, Applied Machine Learning, Matlab, Regression Analysis, Predictive Modeling, Classification And Regression Tree (CART), Machine Learning, Predictive Analytics, Feature Engineering, Statistical Modeling, Data Processing, Sampling (Statistics)

      4.8
      Rating, 4.8 out of 5 stars
      ·
      118 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      Universidade de São Paulo

      Estatística não-paramétrica para a tomada de decisão

      Skills you'll gain: Sample Size Determination, Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Statistical Methods, Data-Driven Decision-Making, Quantitative Research, Statistical Analysis, Decision Making, Probability Distribution

      4.4
      Rating, 4.4 out of 5 stars
      ·
      138 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Methods for Solving Problems

      Skills you'll gain: Problem Solving, Computational Thinking, Probability Distribution, Behavioral Economics, Logical Reasoning, Algorithms, Analytical Skills, Psychology

      4.5
      Rating, 4.5 out of 5 stars
      ·
      181 reviews

      Beginner · Course · 1 - 4 Weeks

    • P

      Packt

      Computer Vision: YOLO Custom Object Detection with Colab GPU

      Skills you'll gain: Image Analysis, Computer Vision, Python Programming, Deep Learning, Real Time Data, Data Processing, Applied Machine Learning, Development Environment, Cloud Storage, Data Collection, Software Installation, System Configuration

      3
      Rating, 3 out of 5 stars
      ·
      8 reviews

      Beginner · Course · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Simulation Models for Decision Making

      Skills you'll gain: Simulations, Probability Distribution, Probability, Statistics, Business Mathematics, Microsoft Excel, Operations Research, Complex Problem Solving, Business Modeling, Risk Modeling, Financial Modeling, Data Modeling, Strategic Thinking, Analysis, Statistical Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      55 reviews

      Beginner · 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

    • How Diffusion Models Work: DeepLearning.AI
    • Statistics For Data Science: Coursera Project Network
    • Data Analysis with Python Project : University of Colorado Boulder
    • ML Parameters Optimization: GridSearch, Bayesian, Random: Coursera Project Network
    • No-Code Machine Learning Using Amazon AWS SageMaker Canvas: Packt
    • Introduction to AI: Key Concepts and Applications: Johns Hopkins University
    • Deep Learning with Real-World Projects: Packt
    • Predictive Modeling and Machine Learning with MATLAB: MathWorks
    • Estatística não-paramétrica para a tomada de decisão: Universidade de São Paulo
    • Methods for Solving Problems: 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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