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

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Explore the Random Forest Course Catalog

Packt
Cluster Analysis and Unsupervised Machine Learning in Python
Skills you'll gain: Unsupervised Learning, Data Visualization, Machine Learning, Machine Learning Algorithms, Scikit Learn (Machine Learning Library), Data-Driven Decision-Making, Exploratory Data Analysis, Data Science, Statistical Methods, Algorithms, NumPy, Python Programming, Development Environment
Intermediate · Course · 1 - 3 Months
École Polytechnique Fédérale de Lausanne
Geographical Information Systems - Part 2
Skills you'll gain: Spatial Analysis, Spatial Data Analysis, GIS Software, Geographic Information Systems, Geospatial Mapping, Geostatistics, Interactive Data Visualization, Data Integration, Data Mapping, Augmented Reality, Statistical Analysis, Sampling (Statistics)
Beginner · Course · 1 - 3 Months
University of Colorado System
Nonlinear Kalman Filters (and Parameter Estimation)
Skills you'll gain: Matlab, Estimation, Engineering Calculations, Numerical Analysis, Performance Tuning, Mathematical Modeling, Simulation and Simulation Software, Differential Equations, Engineering Analysis, System Implementation, Probability Distribution
Intermediate · Course · 1 - 4 Weeks
Johns Hopkins University
Nouvelles approches pour mesurer la santé de la population
Skills you'll gain: Survey Creation, Epidemiology, Data Collection, Sampling (Statistics), Health Assessment, Program Evaluation, Research Design, Health Information Management and Medical Records, Research Methodologies, Data Ethics, Social Justice, Diversity Awareness, Information Systems
Beginner · Course · 1 - 4 Weeks
American Psychological Association
t-Tests of Hypotheses About Means
Skills you'll gain: Sample Size Determination, Statistical Hypothesis Testing, Statistical Methods, Statistical Reporting, Quantitative Research, Statistical Analysis, Probability & Statistics, Statistical Inference, Descriptive Statistics, Experimentation, Probability Distribution, Data Analysis
Beginner · Course · 1 - 3 Months
University of Michigan
Data-Oriented Python Programming and Debugging
Skills you'll gain: Debugging, NumPy, Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Pandas (Python Package), Data Structures, Probability & Statistics, Probability, Probability Distribution, Statistical Analysis, Data Analysis, Program Development, Scientific Visualization, Code Review, Data Manipulation, Programming Principles, Computer Programming, Jupyter, Python Programming
Intermediate · Specialization · 3 - 6 Months
Johns Hopkins University
Data Science Decisions in Time: Using Causal Information
Skills you'll gain: Clinical Trials, Data Analysis, Data-Driven Decision-Making, Analytics, Business Analytics, Decision Making, Random Forest Algorithm, Medical Science and Research, Treatment Planning, Personalized Service, Machine Learning
Intermediate · Course · 1 - 3 Months
Status: Free
Duke University
Inferenzstatistik
Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Statistical Reporting, Statistical Analysis, Data Analysis, Probability & Statistics, Statistical Methods, Statistical Software, R Programming, Sampling (Statistics), Probability Distribution, Software Installation
Beginner · Course · 1 - 3 Months
Packt
Fundamentals of Unity Android Game Development
Skills you'll gain: Android Development, User Interface (UI), User Interface (UI) Design, Mobile Development, Application Development, Program Development, Visualization (Computer Graphics), Graphics Software, Computer Graphics, Graphical Tools, Development Environment, Software Installation, Application Deployment, C# (Programming Language), Software Development
Beginner · Course · 1 - 3 Months
University of Illinois Urbana-Champaign
Untersuchen und Erstellen von Daten für Unternehmen
Skills you'll gain: Descriptive Statistics, Statistical Inference, Sampling (Statistics), Data Analysis, Probability & Statistics, Data Presentation, Statistical Analysis, Quantitative Research, Microsoft Excel, Exploratory Data Analysis, Statistical Visualization, Probability Distribution
Beginner · Course · 1 - 3 Months
University of Michigan
Statistics with Python Using NumPy, Pandas, and SciPy
Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Probability & Statistics, NumPy, Probability, Probability Distribution, Statistical Analysis, Data Analysis, Exploratory Data Analysis, Histogram, Scatter Plots, Regression Analysis, Pandas (Python Package), Linear Algebra, Python Programming
Intermediate · Course · 1 - 4 Weeks
Packt
Intermediate Data Manipulation and Machine Learning
Skills you'll gain: Regression Analysis, Dimensionality Reduction, Reinforcement Learning, Artificial Intelligence, Machine Learning, Applied Machine Learning, Classification And Regression Tree (CART), Statistical Analysis, Predictive Modeling, Supervised Learning, Unsupervised Learning, Feature Engineering, Data Science, Random Forest Algorithm, Data Manipulation
Intermediate · Course · 3 - 6 Months

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

Cluster Analysis and Unsupervised Machine Learning in Python: Packt
Geographical Information Systems - Part 2: École Polytechnique Fédérale de Lausanne
Nonlinear Kalman Filters (and Parameter Estimation): University of Colorado System
Nouvelles approches pour mesurer la santé de la population: Johns Hopkins University
t-Tests of Hypotheses About Means: American Psychological Association
Data-Oriented Python Programming and Debugging: University of Michigan
Data Science Decisions in Time: Using Causal Information: Johns Hopkins University
Inferenzstatistik: Duke University
Fundamentals of Unity Android Game Development: Packt
Untersuchen und Erstellen von Daten für Unternehmen: University of Illinois Urbana-Champaign

Random Forest Courses Online