Random Forest courses can help you learn decision tree algorithms, ensemble methods, feature selection, and model evaluation techniques. You can build skills in data preprocessing, hyperparameter tuning, and interpreting model outputs. Many courses introduce tools like Python's scikit-learn and R's randomForest package, showing how these skills are applied to tasks such as classification, regression, and handling large datasets.

Skills you'll gain: Matplotlib, Applied Machine Learning, Random Forest Algorithm, Predictive Modeling, Predictive Analytics, Machine Learning Algorithms, Data Visualization, Machine Learning, Programming Principles, Data Manipulation, Feature Engineering, Data Cleansing, Supervised Learning, Python Programming, Data Science, Data Processing, NumPy, Pandas (Python Package)
Beginner · Course · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming
Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Feature Engineering, Applied Machine Learning, Advanced Analytics, Machine Learning, Unsupervised Learning, Workflow Management, Data Ethics, Supervised Learning, Data Validation, Classification And Regression Tree (CART), Random Forest Algorithm, Decision Tree Learning, Python Programming, Performance Tuning
Advanced · Course · 1 - 3 Months

Skills you'll gain: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Presentation, Data Visualization Software, Feature Engineering, Regression Analysis, Descriptive Statistics, Statistical Hypothesis Testing, Advanced Analytics, Data Analysis, Tableau Software, Data Science, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming
Build toward a degree
Advanced · Professional Certificate · 3 - 6 Months

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
Intermediate · Course · 1 - 3 Months
University of Michigan
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, Python Programming, Dimensionality Reduction, Random Forest Algorithm, Regression Analysis
Intermediate · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Predictive Modeling, Machine Learning Algorithms, Feature Engineering, Supervised Learning, Classification And Regression Tree (CART), Predictive Analytics, Applied Machine Learning, R Programming, Machine Learning, Random Forest Algorithm, Regression Analysis, Data Processing, Data Collection
Mixed · Course · 1 - 4 Weeks

LearnQuest
Skills you'll gain: Random Forest Algorithm, Keras (Neural Network Library), Classification And Regression Tree (CART), Tensorflow, Deep Learning, Artificial Neural Networks, Predictive Modeling, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning, Regression Analysis, Python Programming
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Supervised Learning, Random Forest Algorithm, Applied Machine Learning, Data Processing, Classification And Regression Tree (CART), Decision Tree Learning, Feature Engineering, Machine Learning Algorithms, Predictive Modeling, Performance Testing, Data Analysis, Scikit Learn (Machine Learning Library), Python Programming
Mixed · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Classification And Regression Tree (CART), Machine Learning, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Supervised Learning, Deep Learning, Random Forest Algorithm, Artificial Neural Networks, Performance Tuning
Beginner · Course · 1 - 4 Weeks
University of Washington
Skills you'll gain: Unsupervised Learning, Supervised Learning, Predictive Analytics, Statistical Modeling, R Programming, Statistical Methods, Decision Tree Learning, Statistical Inference, Statistical Analysis, Machine Learning Algorithms, Machine Learning, Graph Theory, Probability & Statistics, Network Analysis, Big Data, Sampling (Statistics), Random Forest Algorithm
Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Sampling (Statistics), Matplotlib, Data Analysis, Data Mining, Statistical Analysis, Statistical Hypothesis Testing, NumPy, Pandas (Python Package), Probability Distribution, Dimensionality Reduction, R Programming, Probability, Python Programming, Scikit Learn (Machine Learning Library), Linear Algebra, Applied Machine Learning, Unsupervised Learning, Regression Analysis, Statistical Methods, Artificial Intelligence and Machine Learning (AI/ML)
Beginner · Specialization · 3 - 6 Months
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.‎