Welcome to the machine learning course repository! This repository contains Jupyter Notebook files covering various topics in machine learning. Each notebook provides explanations, code examples, and demonstrations of different machine learning algorithms and techniques.
I will continue to update this repository with additional notebooks covering various machine-learning models and algorithms.
01. How_Regression_Works:
- This notebook explains the fundamental concepts of regression analysis, including how regression models work and their applications in predictive modeling.
02. Gradient_Descent:
- A fundamental optimization algorithm used in machine learning. This notebook explores how gradient descent works and its variants.
03. Linear_Regression:
- A simple yet powerful algorithm for modeling the relationship between dependent and independent variables. This notebook covers the basics of linear regression and its implementation in Python.
04. Polynomial_Regression:
- It extends linear regression by fitting a polynomial function to the data. This notebook demonstrates how to perform polynomial regression and its applications.
05. Local_regression:
- Also known as LOESS (Locally Weighted Scatterplot Smoothing), is a non-parametric method for fitting flexible curves to data points. This notebook explores the concepts and implementation of local regression.
06. Logistic_Regression:
- A classification algorithm used to model the probability of a binary outcome. This notebook covers the theory and implementation of logistic regression.
07. Logistic_Regression_Multiclass:
- Extending logistic regression to handle multi-class classification problems. This notebook explains how to generalize logistic regression for multi-class classification tasks.
08. Logistic_Regression_NonLinear:
- Logistic regression can also model non-linear relationships by incorporating non-linear transformations of input features. This notebook demonstrates non-linear logistic regression techniques.
09. Generative_Models_for_Classification:
- Generative models are used to model the joint probability distribution of input features and class labels. This notebook explores generative models for classification tasks.
10. Support_Vector_Machines:
- Support vector machines (SVMs) are powerful supervised learning models for classification and regression tasks. This notebook covers the theory and implementation of SVMs.
11. KFold_Cross_Validation:
- Cross-validation is a technique used to evaluate the performance of machine learning models. This notebook explains K-fold cross-validation and its importance in model evaluation.
12. K-Nearest_Neighbors:
- K-nearest neighbors (KNN) is a simple yet effective algorithm for classification and regression tasks. This notebook introduces the KNN algorithm and its implementation.
13. Tree_Based_Methods:
- Tree-based methods, such as decision trees and random forests, are powerful algorithms for classification and regression tasks. This notebook explores the concepts and implementation of tree-based methods.
14. How_K-means_works:
- K-means clustering is an unsupervised learning algorithm partitioning data into clusters. This notebook explains how the K-means algorithm works.
15. K-Means_Clustering:
- Practical implementation of the K-means clustering algorithm using Python. This notebook demonstrates how to use Silhouette Analysis to evaluate the quality of clusters created by clustering algorithms.
16. Hierarchical_Clustering:
- Hierarchical clustering is another approach to clustering data based on their similarity. This notebook covers hierarchical clustering techniques and their applications.
17. Principal_Components_Analysis_(PCA):
- Principal component analysis (PCA) is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving its variance. This notebook explores PCA and its applications.
Your feedback is valuable to us! If you have any suggestions, find errors, or want to contribute improvements, feel free to open an issue or submit a pull request.
Happy learning! 🚀