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  • Random Forest Algorithm


Results for "random+forest+algorithm"


  • C

    Coursera

    Support Vector Machines in Python, From Start to Finish

    Skills you'll gain: Scikit Learn (Machine Learning Library), Tensorflow, Classification Algorithms, Supervised Learning, Applied Machine Learning, Python Programming, Feature Engineering, Data Preprocessing, Data Science, Machine Learning, Model Evaluation, Data Manipulation, Data Visualization

    4.6
    Rating, 4.6 out of 5 stars
    ·
    162 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    E

    EDUCBA

    Develop a Movie Recommendation Engine

    Skills you'll gain: Feature Engineering, Applied Machine Learning, Machine Learning, Data Preprocessing, Python Programming, Predictive Modeling, Development Environment, Data Manipulation, Exploratory Data Analysis

    4.8
    Rating, 4.8 out of 5 stars
    ·
    10 reviews

    Intermediate · Course · 1 - 4 Weeks

  • P

    Packt

    No-Code Machine Learning Using Amazon AWS SageMaker Canvas

    Skills you'll gain: AWS SageMaker, Applied Machine Learning, Model Evaluation, Amazon Web Services, No-Code Development, Machine Learning, Supervised Learning, Cloud Platforms, Predictive Modeling, Amazon S3, Version Control, Data Preprocessing, Model Deployment

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Alberta

    A Complete Reinforcement Learning System (Capstone)

    Skills you'll gain: Reinforcement Learning, Artificial Intelligence and Machine Learning (AI/ML), Simulations, Model Evaluation, Performance Testing, Artificial Neural Networks, Machine Learning, Machine Learning Algorithms, Markov Model, Algorithms, Performance Tuning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    650 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Preview
    Preview
    E

    EDUCBA

    R: Apply & Analyze K-Means Clustering for Unsupervised ML

    Skills you'll gain: Unsupervised Learning, Customer Analysis, Applied Machine Learning, R Programming, Data Preprocessing, Statistical Machine Learning, Machine Learning, Feature Engineering, Data Analysis

    Mixed · Course · 1 - 4 Weeks

  • I

    IBM

    Scalable Machine Learning on Big Data using Apache Spark

    Skills you'll gain: Apache Spark, PySpark, Applied Machine Learning, Big Data, Machine Learning Methods, Data Storage Technologies, Data Preprocessing, Data Storage, Machine Learning Algorithms, Machine Learning, Distributed Computing, Data Processing, Data Science, Statistical Methods, Model Evaluation, Descriptive Statistics

    3.8
    Rating, 3.8 out of 5 stars
    ·
    1.3K reviews

    Intermediate · Course · 1 - 4 Weeks

  • C

    Coursera

    Interpretable Machine Learning Applications: Part 1

    Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Model Evaluation, Random Forest Algorithm, Responsible AI, Data Import/Export, Machine Learning, Classification Algorithms

    4.4
    Rating, 4.4 out of 5 stars
    ·
    77 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Classification Trees in Python, From Start To Finish

    Skills you'll gain: Decision Tree Learning, Data Preprocessing, Data Transformation, Supervised Learning, Feature Engineering, Scikit Learn (Machine Learning Library), Classification Algorithms, Model Evaluation, Pandas (Python Package)

    4.6
    Rating, 4.6 out of 5 stars
    ·
    233 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • P

    Packt

    Clustering and Classification with Machine Learning in R

    Skills you'll gain: Unsupervised Learning, Dimensionality Reduction, Supervised Learning, R Programming, Applied Machine Learning, R (Software), Classification Algorithms, Machine Learning, Decision Tree Learning, Data Science, Ggplot2, Feature Engineering, Data Preprocessing, Statistical Programming, Predictive Modeling, Data Manipulation, Model Evaluation, Regression Analysis

    Beginner · Course · 1 - 3 Months

  • C

    Coursera

    ML Parameters Optimization: GridSearch, Bayesian, Random

    Skills you'll gain: Model Evaluation, Scikit Learn (Machine Learning Library), Regression Analysis, Performance Tuning, Applied Machine Learning, Machine Learning Methods, Machine Learning, Bayesian Statistics, Data Analysis

    4.9
    Rating, 4.9 out of 5 stars
    ·
    8 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    Machine Learning with R: Build, Analyze & Predict

    Skills you'll gain: Data Analysis, Statistical Analysis, Probability Distribution, R Programming, Exploratory Data Analysis, Statistical Modeling, Machine Learning, Statistical Inference, Classification Algorithms, Data Manipulation, Supervised Learning, Random Forest Algorithm, Regression Analysis, Model Evaluation, Decision Tree Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    15 reviews

    Mixed · Course · 1 - 4 Weeks

  • 2

    28DIGITAL

    Approximation Algorithms

    Skills you'll gain: Algorithms, Graph Theory, Computational Thinking, Theoretical Computer Science, Operations Research, Linear Algebra

    4.7
    Rating, 4.7 out of 5 stars
    ·
    33 reviews

    Intermediate · Course · 1 - 4 Weeks

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

  • Support Vector Machines in Python, From Start to Finish: Coursera
  • Develop a Movie Recommendation Engine: EDUCBA
  • No-Code Machine Learning Using Amazon AWS SageMaker Canvas: Packt
  • A Complete Reinforcement Learning System (Capstone): University of Alberta
  • R: Apply & Analyze K-Means Clustering for Unsupervised ML: EDUCBA
  • Scalable Machine Learning on Big Data using Apache Spark: IBM
  • Interpretable Machine Learning Applications: Part 1: Coursera
  • Classification Trees in Python, From Start To Finish: Coursera
  • Clustering and Classification with Machine Learning in R: Packt
  • ML Parameters Optimization: GridSearch, Bayesian, Random: Coursera

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