Random Forest 课程
Random Forest 课程可以帮助您学习决策树算法、Ensemble 方法、Feature Selection 和模型评估技术。您可以掌握数据预处理、超参数调整和解释模型输出的技能。许多课程会介绍 Python 的 Scikit-learn 和 R 的 randomForest 包等工具，展示如何将这些技能应用于分类、回归和处理大型数据集等任务。
热门 Random Forest 课程和认证
您将获得的技能: Matplotlib, Applied Machine Learning, Random Forest Algorithm, Predictive Modeling, Data Visualization, Data Preprocessing, Machine Learning, Data Manipulation, Feature Engineering, Data Cleansing, Python Programming, Data Science, Model Evaluation, Classification Algorithms, NumPy, Pandas (Python Package)
状态：免费试用多位教师
您将获得的技能: 模型评估, 人工智能, 决策树学习, 深度学习, 无监督学习, Scikit-learn （机器学习库）, 张力流, 数据预处理, 监督学习, Jupyter, 强化学习, 随机森林算法, 迁移学习, 预测建模, 数据伦理, 应用机器学习, 分类算法, NumPy, 功能工程, 机器学习
状态：免费试用Johns Hopkins University
您将获得的技能: 模型评估, 数据预处理, 监督学习, 回归分析, 随机森林算法, 机器学习算法, 预测分析, 预测建模, 数据整理, 统计机器学习, 应用机器学习, 分类算法, R 语言程序设计（中文版）, 功能工程, 机器学习, 探索性数据分析
状态：免费试用您将获得的技能: Feature Engineering, Model Evaluation, Applied Machine Learning, Advanced Analytics, Analytics, Statistical Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Unsupervised Learning, Machine Learning Algorithms, Workflow Management, Data Ethics, Supervised Learning, Data Preprocessing, Random Forest Algorithm, Decision Tree Learning, Verification And Validation, Python Programming, Classification Algorithms, Performance Tuning
状态：免费试用IBM
您将获得的技能: 模型评估, 决策树学习, 数据预处理, 监督学习, Scikit-learn （机器学习库）, 数据清理, 性能指标, 随机森林算法, 抽样（统计）, 逻辑回归, 预测建模, 功能工程, 分类算法, 机器学习
状态：免费试用您将获得的技能: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Visualization Software, Feature Engineering, Regression Analysis, Descriptive Statistics, Logistic Regression, Statistical Hypothesis Testing, Model Evaluation, Data Analysis, Tableau Software, Data Science, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming
是什么让您今天来到 Coursera？
状态：免费试用LearnQuest
您将获得的技能: Python 程序设计, 模型评估, 深度学习, Scikit-learn （机器学习库）, 张力流, 决策树学习, 回归分析, Keras（神经网络库）, 随机森林算法, 分类算法, 机器学习, 人工神经网络
状态：免费试用University of Michigan
您将获得的技能: Python 程序设计, 模型评估, 决策树学习, 无监督学习, Scikit-learn （机器学习库）, 回归分析, 监督学习, 随机森林算法, 预测建模, 功能工程, 应用机器学习, 分类算法, 机器学习, 人工神经网络
状态：新状态：预览您将获得的技能: Model Evaluation, Supervised Learning, Data Preprocessing, Random Forest Algorithm, Applied Machine Learning, Decision Tree Learning, Feature Engineering, Machine Learning Algorithms, Classification Algorithms, Predictive Modeling, Data Analysis, Python Programming
状态：免费试用University of Colorado Boulder
您将获得的技能: Model Evaluation, Applied Machine Learning, Unsupervised Learning, Classification And Regression Tree (CART), Decision Tree Learning, Artificial Neural Networks, Classification Algorithms, Supervised Learning, Machine Learning Algorithms, Random Forest Algorithm, Predictive Modeling, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Statistics
状态：预览Johns Hopkins University
您将获得的技能: Probability & Statistics, Probability Distribution, Simulations, Statistical Modeling, Correlation Analysis, Engineering Analysis, Digital Signal Processing, Statistical Analysis, Reliability, Engineering, Spatial Analysis
状态：免费试用DeepLearning.AI
您将获得的技能: 人工智能, 模型评估, 决策树学习, 深度学习, 张力流, 随机森林算法, 监督学习, 迁移学习, 逻辑回归, 数据伦理, 应用机器学习, 分类算法, 性能调整, 机器学习, 人工神经网络
是什么让您今天来到 Coursera？
总之，以下是 10 最受欢迎的 random forest 课程
- Machine Learning: Random Forest with Python from Scratch©: Packt
- 机器学习: DeepLearning.AI
- 实用机器学习: Johns Hopkins University
- The Nuts and Bolts of Machine Learning: Google
- 有监督的机器学习：分类: IBM
- Google Advanced Data Analytics: Google
- 神经网络和随机森林: LearnQuest
- Python 中的应用机器学习: University of Michigan
- Python: Implement & Evaluate Random Forests for ML: EDUCBA
- Trees, SVM and Unsupervised Learning: University of Colorado Boulder
