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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

    • P

      Packt

      Machine Learning: Random Forest with Python from Scratch©

      Skills you'll gain: Applied Machine Learning, Random Forest Algorithm, Predictive Modeling, Matplotlib, Predictive Analytics, Machine Learning, Data Visualization, Supervised Learning, Data Manipulation, Python Programming, Data Processing, Data Cleansing, Pandas (Python Package), NumPy

      Beginner · Course · 1 - 3 Months

    • D
      S

      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), Artificial Intelligence, NumPy, Predictive Modeling, Deep Learning, Reinforcement Learning, Random Forest Algorithm, Feature Engineering

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      34K reviews

      Beginner · Specialization · 1 - 3 Months

    • D

      DeepLearning.AI

      Advanced Learning Algorithms

      Skills you'll gain: Classification And Regression Tree (CART), Machine Learning Algorithms, Machine Learning, Applied Machine Learning, Data Ethics, Decision Tree Learning, Tensorflow, Artificial Intelligence, Supervised Learning, Deep Learning, Random Forest Algorithm, Artificial Neural Networks, Performance Tuning

      4.9
      Rating, 4.9 out of 5 stars
      ·
      7.8K reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      Machine Learning with Python

      Skills you'll gain: Supervised Learning, Feature Engineering, Jupyter, Unsupervised Learning, Scikit Learn (Machine Learning Library), Python Programming, Predictive Modeling, Machine Learning, Dimensionality Reduction, Classification And Regression Tree (CART), Matplotlib, NumPy, Regression Analysis, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      17K reviews

      Intermediate · Course · 1 - 3 Months

    • G

      Google

      The Nuts and Bolts of Machine Learning

      Skills you'll gain: Feature Engineering, Advanced Analytics, Scikit Learn (Machine Learning Library), Predictive Modeling, Unsupervised Learning, Data Ethics, Machine Learning, Machine Learning Algorithms, Supervised Learning, Data Analysis, Performance Tuning, Python Programming

      4.8
      Rating, 4.8 out of 5 stars
      ·
      514 reviews

      Advanced · Course · 1 - 3 Months

    • U

      University of Michigan

      Applied Machine Learning in Python

      Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Dimensionality Reduction, Random Forest Algorithm

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Practical Machine Learning

      Skills you'll gain: Predictive Modeling, Machine Learning Algorithms, Statistical Machine Learning, Feature Engineering, Supervised Learning, Classification And Regression Tree (CART), Applied Machine Learning, Decision Tree Learning, Machine Learning, Random Forest Algorithm, Regression Analysis, Data Processing, Data Collection

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.3K reviews

      Mixed · Course · 1 - 4 Weeks

    • I

      IBM

      Supervised Machine Learning: Classification

      Skills you'll gain: Supervised Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Applied Machine Learning, Predictive Modeling, Scikit Learn (Machine Learning Library), Data Processing, Data Cleansing, Machine Learning, Regression Analysis, Data Manipulation, Business Analytics, Feature Engineering, Random Forest Algorithm, Statistical Modeling, Sampling (Statistics), Performance Metric

      4.8
      Rating, 4.8 out of 5 stars
      ·
      410 reviews

      Intermediate · Course · 1 - 3 Months

    • I

      IBM

      Unsupervised Machine Learning

      Skills you'll gain: Unsupervised Learning, Dimensionality Reduction, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Feature Engineering, Machine Learning, Statistical Machine Learning, Text Mining, Data Mining, Data Science, Big Data, NumPy, Data Analysis, Algorithms, Natural Language Processing, Linear Algebra

      4.7
      Rating, 4.7 out of 5 stars
      ·
      318 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Washington

      Machine Learning Foundations: A Case Study Approach

      Skills you'll gain: Applied Machine Learning, Feature Engineering, Regression Analysis, Machine Learning, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Supervised Learning, Artificial Intelligence, Deep Learning, Classification And Regression Tree (CART), Computer Vision, Application Development, Predictive Modeling, Natural Language Processing, Text Mining, Data Mining, Information Architecture

      4.6
      Rating, 4.6 out of 5 stars
      ·
      14K reviews

      Mixed · Course · 1 - 3 Months

    • I

      Imperial College London

      Mathematics for Machine Learning: PCA

      Skills you'll gain: Dimensionality Reduction, NumPy, Probability & Statistics, Feature Engineering, Jupyter, Data Science, Statistics, Linear Algebra, Python Programming, Advanced Mathematics, Machine Learning, Calculus

      4
      Rating, 4 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • N
      G
      N
      G

      Multiple educators

      Machine Learning for Trading

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning, Google Cloud Platform, Applied Machine Learning, Financial Trading, Reinforcement Learning, Supervised Learning, Data Pipelines, Time Series Analysis and Forecasting, Statistical Machine Learning, Technical Analysis, Deep Learning, Portfolio Management, Machine Learning Methods, Artificial Neural Networks, Market Trend, Securities Trading, Artificial Intelligence and Machine Learning (AI/ML), Financial Market

      3.9
      Rating, 3.9 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Specialization · 1 - 3 Months

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    1234…33

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

    • Machine Learning: Random Forest with Python from Scratch©: Packt
    • Machine Learning: DeepLearning.AI
    • Advanced Learning Algorithms: DeepLearning.AI
    • Machine Learning with Python: IBM
    • The Nuts and Bolts of Machine Learning: Google
    • Applied Machine Learning in Python: University of Michigan
    • Practical Machine Learning: Johns Hopkins University
    • Supervised Machine Learning: Classification: IBM
    • Unsupervised Machine Learning: IBM
    • Machine Learning Foundations: A Case Study Approach: University of Washington

    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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