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What is Gaussian Naive Bayes Algorithm Gaussian Naive Bayes Implementation Intellipaat

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Lesson extensions

Lessons List | 19 Lesson
What Is Scikit Learn Introduction To Scikit Learn Machine Learning Tutorial Intellipaat
00:13:01

What Is Scikit Learn Introduction To Scikit Learn Machine Learning Tutorial Intellipaat
Scikit Learn Tutorial Scikit Learn Workflow Data Preprocessing In Machine Learning Intellipaat
00:57:10

Scikit Learn Tutorial Scikit Learn Workflow Data Preprocessing In Machine Learning Intellipaat
Classification in Machine Learning Machine Learning with Scikit Learn Intellipaat
00:34:41

Classification in Machine Learning Machine Learning with Scikit Learn Intellipaat
KNN Algorithm in Machine Learning K Nearest Neighbor Scikit Learn Tutorial Intellipaat
00:27:15

KNN Algorithm in Machine Learning K Nearest Neighbor Scikit Learn Tutorial Intellipaat
SVM Algorithm in Machine Learning Support Vector Machine Scikit Learn Tutorial Intellipaat
00:28:55

SVM Algorithm in Machine Learning Support Vector Machine Scikit Learn Tutorial Intellipaat
Linear Regression Algorithm Linear Regression in Scikit Learn Intellipaat
00:24:47

Linear Regression Algorithm Linear Regression in Scikit Learn Intellipaat
Logistic Regression in Machine Learning Logistic Regression Demo Sklearn Tutorial Intellipaat
00:48:10

Logistic Regression in Machine Learning Logistic Regression Demo Sklearn Tutorial Intellipaat
K Means Algorithm in Machine Learning K Means Clustering Scikit Learn Tutorial Intellipaat
00:24:47

K Means Algorithm in Machine Learning K Means Clustering Scikit Learn Tutorial Intellipaat
What is Gaussian Naive Bayes Algorithm Gaussian Naive Bayes Implementation Intellipaat
00:45:45

What is Gaussian Naive Bayes Algorithm Gaussian Naive Bayes Implementation Intellipaat
Decision Tree Machine Learning Decision Tree Algorithm Machine Learning Intellipaat
00:48:17

Decision Tree Machine Learning Decision Tree Algorithm Machine Learning Intellipaat
Random Forest Explained Random Forest Algorithm in Machine Learning Data Science Intellipaat
00:54:34

Random Forest Explained Random Forest Algorithm in Machine Learning Data Science Intellipaat
AdaBoost Algorithm In Machine Learning Theory AdaBoost Step By Step Explanation Intellipaat
00:26:48

AdaBoost Algorithm In Machine Learning Theory AdaBoost Step By Step Explanation Intellipaat
AdaBoost Algorithm Python Implementation AdaBoost Python Tutorial Machine Learning Intellipaat
00:35:14

AdaBoost Algorithm Python Implementation AdaBoost Python Tutorial Machine Learning Intellipaat
Gradient Boosting Machine Learning Gradient Boosting for Regression Explained Intellipaat
00:48:58

Gradient Boosting Machine Learning Gradient Boosting for Regression Explained Intellipaat
K Means Clustering Algorithm K Means Clustering Python Machine Learning Tutorial Intellipaat
00:27:25

K Means Clustering Algorithm K Means Clustering Python Machine Learning Tutorial Intellipaat
GridSearchCV Grid Search Hyper Parameter Tuning Scikit Learn Tutorial Intellipaat
00:22:39

GridSearchCV Grid Search Hyper Parameter Tuning Scikit Learn Tutorial Intellipaat
Mean Shift Clustering How Mean Shift Clustering Works Scikit Learn Tutorial Intellipaat
00:32:50

Mean Shift Clustering How Mean Shift Clustering Works Scikit Learn Tutorial Intellipaat
Ensemble Learning in Machine Learning Ensemble Learning Tutorial Machine Learning Intellipaat
00:09:14

Ensemble Learning in Machine Learning Ensemble Learning Tutorial Machine Learning Intellipaat
DBSCAN Clustering Algorithm Density Based Clustering DBSCAN Implementation Intellipaat
00:33:00

DBSCAN Clustering Algorithm Density Based Clustering DBSCAN Implementation Intellipaat

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Sanika Gajarishi

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Course Description

Random forest algorithm course, in this course participants will delve into the intricacies of Random Forests, understanding how they harness the collective wisdom of multiple decision trees to deliver robust predictions. Starting with the fundamentals, learners will uncover the mechanics of building individual decision trees within the Random Forest ensemble, exploring concepts like feature sampling and bootstrapping. Through practical exercises and real-world examples, students will grasp how Random Forests mitigate overfitting and handle noisy datasets, making them versatile tools for various domains. Furthermore, the course delves into advanced topics such as hyperparameter tuning and feature importance analysis, empowering participants to optimize and interpret Random Forest models effectively. By the end of the course, students will possess the knowledge and skills to leverage Random Forest Algorithm confidently for predictive modeling tasks, making informed decisions and extracting valuable insights from data.
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