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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

    • E

      EIT Digital

      I/O-efficient algorithms

      Skills you'll gain: Data Structures, Theoretical Computer Science, Data Storage Technologies, Algorithms, Graph Theory, File Systems, Data Access, Performance Tuning, Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      60 reviews

      Advanced · Course · 1 - 3 Months

    • C

      Coursera Project Network

      TensorFlow Prediction: Identify Penguin Species

      Skills you'll gain: Data Processing, Tensorflow, Applied Machine Learning, Feature Engineering, Data Cleansing, Classification And Regression Tree (CART), Data Manipulation, Machine Learning, Predictive Modeling, Random Forest Algorithm, Pandas (Python Package), Data Analysis, Exploratory Data Analysis

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New
      Status: Free Trial
      Free Trial
      E

      Edureka

      Applied Machine Learning with Python

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Applied Machine Learning, Machine Learning Algorithms, Data-Driven Decision-Making, Regression Analysis, Machine Learning, Scikit Learn (Machine Learning Library), Decision Tree Learning, Data Mining, Python Programming, Analytics, Statistical Modeling, Predictive Modeling, Statistical Methods, Classification And Regression Tree (CART), Statistical Analysis, Random Forest Algorithm, Feature Engineering, Dimensionality Reduction

      Intermediate · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Classify Images of Cats and Dogs using Transfer Learning

      Skills you'll gain: Tensorflow, Image Analysis, Keras (Neural Network Library), Applied Machine Learning, Google Cloud Platform, Deep Learning, Computer Vision

      Beginner · Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Statistical Inference and Hypothesis Testing in Data Science Applications

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Data Ethics, Probability & Statistics, Statistical Inference, Statistical Analysis, Quantitative Research, Statistics, Probability Distribution

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      50 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      Status: Free Trial
      Free Trial
      S

      Simplilearn

      Supervised Learning Regression Classification Clustering

      Skills you'll gain: Supervised Learning, Data Modeling, Data Analysis, Regression Analysis, Unsupervised Learning, Classification And Regression Tree (CART), Machine Learning Algorithms, Machine Learning, Predictive Modeling, Predictive Analytics, Random Forest Algorithm, Bayesian Statistics

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      P

      Packt

      Python Programming for Quantum Computing

      Skills you'll gain: Object Oriented Programming (OOP), Python Programming, Data Structures, Data Manipulation, Computer Programming, Data Processing, Scripting, Software Installation, Development Environment, Jupyter

      4.3
      Rating, 4.3 out of 5 stars
      ·
      10 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      W

      Whizlabs

      NVIDIA: Prompt Engineering and Data Analysis

      Skills you'll gain: Prompt Engineering, Data Visualization, Large Language Modeling, Text Mining, Scatter Plots, Data Visualization Software, Generative AI, Histogram, Performance Tuning, Data Analysis, Natural Language Processing

      Intermediate · Course · 1 - 4 Weeks

    • 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

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Data Science Methods for Quality Improvement

      Skills you'll gain: Process Capability, Statistical Inference, Correlation Analysis, Probability Distribution, Statistical Analysis, Statistical Visualization, Descriptive Statistics, Sampling (Statistics), Statistical Process Controls, Statistics, Data Visualization, Probability & Statistics, Statistical Hypothesis Testing, Data Analysis, Scientific Visualization, Systems Analysis, Data Literacy, Data Analysis Software, R Programming, Quality Control

      Build toward a degree

      4.5
      Rating, 4.5 out of 5 stars
      ·
      49 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Machine Learning and Emerging Technologies in Cybersecurity

      Skills you'll gain: Intrusion Detection and Prevention, Threat Detection, Computer Security Incident Management, Cybersecurity, Incident Response, Applied Machine Learning, Network Security, Machine Learning Algorithms, Machine Learning, Unsupervised Learning, Supervised Learning, Deep Learning, Artificial Neural Networks

      Intermediate · Course · 1 - 3 Months

    • 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

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

    • I/O-efficient algorithms: EIT Digital
    • TensorFlow Prediction: Identify Penguin Species: Coursera Project Network
    • Applied Machine Learning with Python: Edureka
    • Classify Images of Cats and Dogs using Transfer Learning: Google Cloud
    • Statistical Inference and Hypothesis Testing in Data Science Applications: University of Colorado Boulder
    • Supervised Learning Regression Classification Clustering: Simplilearn
    • Python Programming for Quantum Computing: Packt
    • NVIDIA: Prompt Engineering and Data Analysis: Whizlabs
    • Statistical Methods for Computer Science: Johns Hopkins University
    • Data Science Methods for Quality Improvement: 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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