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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

    • É

      École Polytechnique Fédérale de Lausanne

      Functional Program Design in Scala

      Skills you'll gain: Scala Programming, Software Design, Software Design Patterns, Functional Design, Event-Driven Programming, Java, Programming Principles, Performance Tuning, Data Structures, Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Course · 1 - 3 Months

    • 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

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Colorado Boulder

      Introduction to Machine Learning: Supervised Learning

      Skills you'll gain: Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Decision Tree Learning, Scikit Learn (Machine Learning Library), Matplotlib, Random Forest Algorithm, Machine Learning, Predictive Modeling, Data Science, Python Programming, Classification And Regression Tree (CART), Mathematical Modeling, Applied Mathematics, Exploratory Data Analysis, Statistical Programming, Regression Analysis, Feature Engineering, Data Cleansing, Performance Tuning

      Build toward a degree

      3.4
      Rating, 3.4 out of 5 stars
      ·
      75 reviews

      Intermediate · Course · 1 - 3 Months

    • P

      Packt

      Computer Vision: YOLO Custom Object Detection with Colab GPU

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

      3
      Rating, 3 out of 5 stars
      ·
      8 reviews

      Beginner · Course · 3 - 6 Months

    • D

      Duke University

      Think Again III: How to Reason Inductively

      Skills you'll gain: Deductive Reasoning, Logical Reasoning, Probability, Sampling (Statistics), Statistics, Correlation Analysis, Scientific Methods

      4.6
      Rating, 4.6 out of 5 stars
      ·
      394 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University at Buffalo

      Computer Vision Basics

      Skills you'll gain: Computer Vision, Image Analysis, Computer Graphics, Visualization (Computer Graphics), Data Processing, Digital Design, Artificial Intelligence, Matlab, Linear Algebra, Algorithms, Calculus, Probability & Statistics

      4.2
      Rating, 4.2 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Build and deploy a stroke prediction model using R

      Skills you'll gain: Feature Engineering, Predictive Modeling, Predictive Analytics, R Programming, Data Analysis, Statistical Modeling, Statistical Analysis, Data Cleansing, Exploratory Data Analysis, Applied Machine Learning, Data Manipulation, Application Deployment, Data Transformation, Machine Learning Methods, Interactive Data Visualization

      4.4
      Rating, 4.4 out of 5 stars
      ·
      17 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Scikit-Learn to Solve Regression Machine Learning Problems

      Skills you'll gain: Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Machine Learning Algorithms, Applied Machine Learning, Python Programming, Machine Learning, Random Forest Algorithm

      Beginner · Guided Project · Less Than 2 Hours

    • U

      University of Michigan

      Data Collection: Online, Telephone and Face-to-face

      Skills you'll gain: Surveys, Interviewing Skills, Data Collection, Sampling (Statistics), Unstructured Data, Research Methodologies, Data Capture, Qualitative Research, Data Validation, Data Quality

      4.6
      Rating, 4.6 out of 5 stars
      ·
      345 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      UX Research at Scale: Surveys, Analytics, Online Testing

      Skills you'll gain: Surveys, UI/UX Research, User Research, Survey Creation, Sampling (Statistics), A/B Testing, Qualitative Research, Usability Testing, Research Methodologies, Web Analytics and SEO, Data Collection, Research Design, Sample Size Determination, Analytics, Data Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      201 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Minnesota

      Black-box and White-box Testing

      Skills you'll gain: Cucumber (Software), Gherkin (Scripting Language), Software Testing, Testability, Test Case, Behavior-Driven Development, Code Coverage, Acceptance Testing, Unit Testing, Functional Testing, Test Automation, Requirements Analysis, Java Programming

      3.8
      Rating, 3.8 out of 5 stars
      ·
      110 reviews

      Intermediate · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Evaluating Large-Scale Health Programs

      Skills you'll gain: Surveys, Program Evaluation, Survey Creation, Health Policy, Sampling (Statistics), Health Systems, Health Assessment, Systems Thinking, Health Equity, Data Collection, Quantitative Research, Training Programs, Data Analysis, Maternal Health, Data Management, Statistical Analysis, Analysis, Nutrition and Diet, Public Health, Research Design

      4.5
      Rating, 4.5 out of 5 stars
      ·
      64 reviews

      Intermediate · Specialization · 3 - 6 Months

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

    • Functional Program Design in Scala: École Polytechnique Fédérale de Lausanne
    • Deep Learning with Real-World Projects: Packt
    • Introduction to Machine Learning: Supervised Learning: University of Colorado Boulder
    • Computer Vision: YOLO Custom Object Detection with Colab GPU: Packt
    • Think Again III: How to Reason Inductively: Duke University
    • Computer Vision Basics: University at Buffalo
    • Build and deploy a stroke prediction model using R: Coursera Project Network
    • Scikit-Learn to Solve Regression Machine Learning Problems: Coursera Project Network
    • Data Collection: Online, Telephone and Face-to-face: University of Michigan
    • UX Research at Scale: Surveys, Analytics, Online Testing: 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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