https://github.com/xredax/machine-learning

This repository includes key concepts and implementations from a machine learning course, covering algorithms like regression, classification, clustering, and ensemble methods. It provides both theoretical insights and practical examples.
https://github.com/xredax/machine-learning

artificial-intelligence course machine-learning python

Last synced: 27 days ago
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This repository includes key concepts and implementations from a machine learning course, covering algorithms like regression, classification, clustering, and ensemble methods. It provides both theoretical insights and practical examples.

• Host: GitHub
• URL: https://github.com/xredax/machine-learning
• Owner: XredaX
• Created: 2024-10-16T15:06:26.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-11-19T00:47:25.000Z (6 months ago)
• Last Synced: 2025-02-15T07:24:22.576Z (3 months ago)
• Topics: artificial-intelligence, course, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 43.3 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Course - Overview

This repository contains an overview and key topics covered in a machine learning course. The course covers a wide range of supervised and unsupervised learning algorithms, with a focus on both theory and practical applications.

## Course Contents

1. **Linear Regression (Single Variable)**: Introduction to basic linear regression using a single feature.

2. **Linear Regression (Multiple Variables)**: Extending linear regression to handle multiple features.

3. **Gradient Descent and Cost Function**: Learning optimization techniques for parameter tuning.

4. **Save Model Using Joblib and Pickle**: Persisting trained models with Joblib and Pickle.

5. **Dummy Variables & One Hot Encoding**: Handling categorical data with dummy variables and one-hot encoding.

6. **Training and Testing Data**: Splitting data into training and testing sets for evaluation.

7. **Logistic Regression (Binary Classification)**: Introduction to logistic regression for binary classification tasks.

8. **Logistic Regression (Multiclass Classification)**: Extending logistic regression to multiclass classification problems.

9. **Decision Tree**: Decision tree algorithms for classification and regression.

10. **SVM (Support Vector Machine)**: Support vector machine algorithm for classification and regression.

11. **Random Forest**: Ensemble learning using the random forest algorithm.

12. **K-Fold Cross Validation**: Cross-validation techniques to evaluate model performance.

13. **K-Means Clustering**: Unsupervised learning using the K-means clustering algorithm.

14. **Naive Bayes Classifier Algorithm**: Naive Bayes algorithm for classification tasks.

15. **Hyperparameter Tuning (GridSearchCV)**: Tuning model hyperparameters using GridSearchCV.

16. **L1 and L2 Regularization (Lasso, Ridge Regression)**: Preventing overfitting with L1 and L2 regularization.

17. **K-Nearest Neighbors (KNN) Classification**: KNN algorithm for classification problems.

18. **Principal Component Analysis (PCA)**: Dimensionality reduction using PCA.

19. **Ensemble Learning - Bagging**: Using ensemble learning techniques, specifically bagging.

## Video Source

For a comprehensive guide and practical walkthrough of the topics listed above, refer to the [YouTube Playlist](https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw).