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Codes and templates for ML algorithms created, modified and optimized in Python and R.

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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