What will you learn in this course?

Develop effective models using supervised learning algorithms for accurate data prediction and classification tasks
Apply techniques like linear regression, decision trees, and support vector machines to real-world datasets
Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score in supervised learning contexts
Implement data preprocessing, feature selection, and feature engineering to improve supervised learning outcomes

Optimize hyperparameters and tune models for enhanced predictive accuracy and robustness
Identify suitable supervised learning algorithms for specific problems like regression and classification
Interpret model results and visualize data relationships to communicate insights effectively
Troubleshoot common issues like overfitting, underfitting, and bias in supervised learning models

How to Get The Certificate

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Lessons | 11

00:09:40

Supervised and Unsupervised Learning In Machine Learning | Machine Learning Tutorial | Simplilearn

00:26:43

Support Vector Machine How Support Vector Machine Works | SVM In Machine Learning | Simplilearn

00:21:03

Support Vector Machine in R | SVM Algorithm Explained with Example | Data Science in R | Simplilearn

00:35:46

Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

00:30:32

Linear Regression in R | Linear Regression in R With Example | Data Science Algorithms | Simplilearn

00:38:17

Logistic Regression | Logistic Regression in Python | Machine Learning Algorithms | Simplilearn

00:19:31

Logistic Regression in R | Logistic Regression in R Example | Data Science Algorithms | Simplilearn

00:43:45

Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn

00:32:40

Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn

00:46:21

Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn

00:27:43

KNN Algorithm In Machine Learning | KNN Algorithm Using Python | K Nearest Neighbor | Simplilearn

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Hicham Salim El Natour

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2025-11-26

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Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets.

Supervised Learning Algorithms

Programming

How to Get The Certificate

Supervised and Unsupervised Learning In Machine Learning | Machine Learning Tutorial | Simplilearn

Support Vector Machine How Support Vector Machine Works | SVM In Machine Learning | Simplilearn

Support Vector Machine in R | SVM Algorithm Explained with Example | Data Science in R | Simplilearn

Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

Linear Regression in R | Linear Regression in R With Example | Data Science Algorithms | Simplilearn

Logistic Regression | Logistic Regression in Python | Machine Learning Algorithms | Simplilearn

Logistic Regression in R | Logistic Regression in R Example | Data Science Algorithms | Simplilearn

Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn

Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn

Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn

KNN Algorithm In Machine Learning | KNN Algorithm Using Python | K Nearest Neighbor | Simplilearn

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