What is 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.

Why is random forest important to learn?

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.

What are typical careers that use random forest?

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.

How can online courses help me learn about random forest?

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.

What are the benefits of taking an online Random Forest 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.

What Random Forest courses are best for training and upskilling employees or the workforce?

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 .

Top Random Forest Courses - Learn Random Forest Online

Explore the Random Forest Course Catalog

Ball State University
Introduction to Data Science
Skills you'll gain: Data Ethics, Data Collection, Probability & Statistics, Data Literacy, Sampling (Statistics), Data Science, R Programming, Data Structures, Data Integrity, Information Privacy, Tidyverse (R Package), Data Manipulation, Ggplot2, Ethical Standards And Conduct
Build toward a degree
Beginner · Course · 1 - 3 Months
Status: Free Trial
Fundação Instituto de Administração
Pesquisa de Mercado e Estratégia de Marketing
Skills you'll gain: Quantitative Research, Qualitative Research, Marketing Analytics, Market Analysis, Market Research, Survey Creation, Exploratory Data Analysis, Marketing Strategies, Sampling (Statistics), Strategic Marketing, Marketing Management, Data Collection, Market Intelligence, Forecasting, Strategic Decision-Making, Data Analysis, Predictive Modeling, Analysis, Data-Driven Decision-Making, Marketing
4.6
Rating, 4.6 out of 5 stars
36 reviews
Beginner · Specialization · 3 - 6 Months
Status: Free Trial
University of California, Santa Cruz
Bayesian Statistics: Capstone Project
Skills you'll gain: Bayesian Statistics, Technical Communication, R Programming, Statistical Analysis, Statistical Modeling, Data Analysis, Advanced Analytics, Time Series Analysis and Forecasting, Markov Model, Statistical Methods, Predictive Modeling, Sampling (Statistics), Probability Distribution
Advanced · Course · 1 - 4 Weeks
Status: Free Trial
University of Colorado Boulder
Data Driven Decision Making
Skills you'll gain: Statistical Hypothesis Testing, Correlation Analysis, Statistical Analysis, Statistical Software, Statistical Methods, Probability & Statistics, Data Analysis, Data Visualization, R Programming, Statistical Inference, Variance Analysis, Sampling (Statistics)
Build toward a degree
4.9
Rating, 4.9 out of 5 stars
27 reviews
Intermediate · Course · 1 - 3 Months
Erasmus University Rotterdam
Necessary Condition Analysis (NCA)
Skills you'll gain: Data Analysis, Statistical Reporting, Quantitative Research, Statistical Analysis, Statistical Software, Small Data, Qualitative Research, R Programming, Sampling (Statistics), Technical Communication, Research Methodologies, Scatter Plots, Statistical Hypothesis Testing
4.9
Rating, 4.9 out of 5 stars
28 reviews
Beginner · Course · 1 - 3 Months
Status: New
Status: Free Trial
Packt
Advanced ML Algorithms & Unsupervised Learning
Skills you'll gain: Dimensionality Reduction, Unsupervised Learning, Deep Learning, Random Forest Algorithm, Machine Learning Algorithms, Machine Learning, Decision Tree Learning, Classification And Regression Tree (CART), Feature Engineering, Supervised Learning, Statistical Machine Learning, Predictive Modeling, Artificial Intelligence, Exploratory Data Analysis
Intermediate · Course · 1 - 3 Months
Status: New
Packt
Cluster Analysis and Unsupervised Machine Learning in Python
Skills you'll gain: Unsupervised Learning, Data Visualization, Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Scikit Learn (Machine Learning Library), Exploratory Data Analysis, Data Science, Statistical Methods, Algorithms, NumPy, Python Programming
Intermediate · Course · 1 - 3 Months
Status: Free Trial
University of Colorado Boulder
Fundamental Tools of Data Wrangling
Skills you'll gain: Pandas (Python Package), Data Wrangling, NumPy, Data Visualization, Data Cleansing, Data Structures, Data Analysis, Data Manipulation, Data Transformation, Data Quality, Exploratory Data Analysis, Data Integration, Programming Principles, Python Programming, Scripting
4.6
Rating, 4.6 out of 5 stars
17 reviews
Beginner · Course · 1 - 3 Months
Status: Free Trial
University of Colorado Boulder
Stability and Capability in Quality Improvement
Skills you'll gain: Process Capability, Statistical Process Controls, Statistical Analysis, Data Analysis Software, R Programming, Quality Control, Statistical Methods, Process Analysis, Data Transformation, Statistical Hypothesis Testing, Process Improvement, Probability Distribution
Build toward a degree
4
Rating, 4 out of 5 stars
15 reviews
Intermediate · Course · 1 - 3 Months
Status: Free Trial
Scrimba
API Basics 1: Build a Bot (Fetch, JSON & Async JavaScript)
Skills you'll gain: JSON, Application Programming Interface (API), Restful API, Javascript, Web Applications, Data Access, Web Servers, Hypertext Markup Language (HTML), Servers, Cascading Style Sheets (CSS)
Intermediate · Course · 1 - 4 Weeks
Coursera Project Network
Interpretable Machine Learning Applications: Part 4
Skills you'll gain: Predictive Modeling, Applied Machine Learning, Data Analysis, Google Cloud Platform, Jupyter, Decision Tree Learning, Data Processing, Exploratory Data Analysis, Machine Learning, Random Forest Algorithm, Statistical Visualization
4.7
Rating, 4.7 out of 5 stars
13 reviews
Intermediate · Guided Project · Less Than 2 Hours
Status: Free Trial
University of Michigan
How to Describe Data
Skills you'll gain: Histogram, Data Visualization, Data Literacy, Data Presentation, Data Collection, Descriptive Statistics, Statistics, Data Analysis, Statistical Visualization, Probability & Statistics, Statistical Reporting, Sampling (Statistics), Data Validation
4.7
Rating, 4.7 out of 5 stars
18 reviews
Beginner · Course · 1 - 4 Weeks