Machine-Learning

Everything in this repository stems from the Machine Learning A-Z course taught by Kirll Eremenko.

Just need to upload these now!

Topics Include

• Data Preprocessing
• Simple and Multiple Linear Regressions
• Polynomial Regressions
• Support Vector Regressions (SVR)
• Decision Tree Regressions
• Random Forest Regressions
• Evaluating Regression Performance
• Logistic Regressions
• K-Nearest Neighbors (k-NN)
• Support Vector Machine (SVM)
• Kernel-SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classification Model Performances
• K-Means Clustering
• Hierarchal Clustering
• Apriori
• Eclat
• Upper Confidence Bound (UCB)
• Thompson Sampling
• Natural Language Processing Algorithms
• Artificial Neural Networks
• Convolutional Neural Networks
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Model Selection
• XGBoost