#100DaysofMLCode

Table of Contents

1. Data Pre-processing

• Importing Libraries
• Importing Data sets
• Handling the missing data values
• Encoding categorical data
• Split Data into Train data and Test data
• Feature Scaling

2. Regression

• Simple Linear Regression
• Multi Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression

3. Classification

• Logistic Regression
• K Nearest Neighbors Classification
• Support Vector Machine
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

4. Clustering

• K-Means Clustering
• Hierarchical Clustering

5. Association Rule

• Apriori
• Eclat

6. Reinforcement Learning

• Upper Confidence Bounds
• Thompson Sampling

7. Natural Language Processing

• AWS Comprehend

8. Deep Learning

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)

9. Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA

10. Model Selection

• Grid Search
• K-fold Cross Validation
• XGBoost

11. Data Visualization

• Matplotlib library in Python
• Tableau
• Power BI
• Grafana

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How to Contribute

This is an open project and contribution in all forms are welcomed. Please follow these Contribution Guidelines

Code of Conduct

Adhere to the GitHub specified community code.

License

Check the official MIT License here.