Machine Learing

Study repo based on tutorials by "Super Data Science".

Each concept have two sub folders at final.

1. Orj - Orignal smaple provided by superdatascience.com
2. MyCode - The code practised by me

Part 1 - Data Preprocessing

1. Data Preprocessing

Part 2 - Regression

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Support Vector Regression (SVR)
5. Decision Tree Regression
6. Random Forest Regression

Part 3 - Classification

1. Logistic Regression
2. K-Nearest Neighbors (K-NN)
3. Support Vector Machine (SVM)
4. Kernel SVM
5. Naive Bayes
6. Decision Tree Classification
7. Random Forest Classification

Part 4 - Clustering

1. K-Means Clustering
2. Hierarchical Clustering

Part 5 - Association Rule Learning

1. Association Rule Learning
2. Apriori
3. Eclat

Part 6 - Reinforcement Learning

1. Upper Confidence Bound (UCB)
2. Thompson Sampling

Part 7 - Natural Language Processing

1. Natural Language Processing

Part 8 - Deep Learning

1. Artificial Neural Networks (ANN)
2. Convolutional Neural Networks (CNN)

Part 9 - Dimensionality Reduction

1. Principal Component Analysis (PCA)
2. Linear Discriminant Analysis (LDA)
3. Kernel PCA

Part 10 - Model Selection & Boosting

1. Model Selection
2. XGBoost