Machine-Learning-Codes-And-Templates

A step towards Data Science and Machine Learning

Codes and templates for ML algorithms created, modified and optimized in Python and R from the SuperDataScience Course by Kirill Ermenko(Data Scientist) and Hadelin de Ponteves(AI Entrepreneur).

Contains the code and implementation of the following topics and techniques:

1. Data Preprocessing
   • Importing the dataset
   • Dealing with missing data
   • Splitting the data into test set and training set
   • Feature Scaling
2. Regression
   • Simple Linear Regression
   • Multiple Linear Regression
   • Polynomial Linear Regression
   • Support Vector Regression (SVR)
   • Decision Tree Regression
   • Random Forest Regression
3. Classification
   • Logistic Regression
   • K-Nearest Neighbors (K-NN)
   • Support Vector Machine (SVM)
   • Kernel SVM
   • Naive Bayes
   • Decision Tree Classifiers
   • Random Forest Classifiers
4. Clustering
   • K-Means Clustering
   • Hierarchical Clustering
5. Association Rule Learning
   • Apriori
   • Eclat
6. Reinforcement Learning
   • Upper Confidence Bound(UCB)
   • Thompson Sampling
7. Natural Language Processing
   • NLP for text analysis and classification.
8. Deep Learning
   • Artificial Neural Networks(ANN)
   • Convolutional Neural Networks(CNN)
9. Dimensionality Reduction
   • Principal Component Analysis(PCA)
   • Linear Discreminant Analysis(LDA)
   • Kernel PCA
10. Model Selection & Boosting
    • Model Selection using K-Fold Cross Validation
    • Parameter Tuning using Grid Search
    • XGBoost