This repository contains Jupyter notebooks implementing various machine learning algorithms covered in the Udemy course Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]. Each notebook provides hands-on implementation of key machine learning techniques using Python.
The course is divided into several sections, and the corresponding Jupyter notebooks for each algorithm are listed below.
- Simple Linear Regression:
simple_linear_regression.ipynb
- Multiple Linear Regression:
multiple_linear_regression.ipynb
- Polynomial Regression:
polynomial_regression.ipynb
- Support Vector Regression (SVR):
support_vector_regression.ipynb
- Decision Tree Regression:
decision_tree_regression.ipynb
- Random Forest Regression:
random_forest_regression.ipynb
- Logistic Regression:
logistic_regression.ipynb
- K-Nearest Neighbors (KNN):
k_nearest_neighbors.ipynb
- Support Vector Machine (SVM):
support_vector_machine.ipynb
- Kernel SVM:
kernel_svm.ipynb
- Naive Bayes:
naive_bayes.ipynb
- Decision Tree Classification:
decision_tree_classification.ipynb
- Random Forest Classification:
random_forest_classification.ipynb
- K-Means Clustering:
k_means_clustering.ipynb
- Hierarchical Clustering:
hierarchical_clustering.ipynb
- Apriori:
apriori.ipynb
- Eclat:
eclat.ipynb
- Upper Confidence Bound (UCB):
upper_confidence_bound.ipynb
- Thompson Sampling:
thompson_sampling.ipynb
- Natural Language Processing (NLP):
natural_language_processing.ipynb
- Artificial Neural Networks (ANN):
artificial_neural_network.ipynb
- Convolutional Neural Networks (CNN):
convolutional_neural_network.ipynb
- Principal Component Analysis (PCA):
principal_component_analysis.ipynb
- Linear Discriminant Analysis (LDA):
linear_discriminant_analysis.ipynb
- Kernel PCA:
kernel_pca.ipynb
- K-Fold Cross Validation:
k_fold_cross_validation.ipynb
- Grid Search:
grid_search.ipynb
- XGBoost:
xg_boost.ipynb