Machine Learning Algorithms & Techniques Lab

Requirements

• Poetry or Anaconda
• Python 3.10

Contents

1 - Data Preprocessing

2 - Regression

• 4 - Simple Linear Regression
• 5 - Multiple Linear Regression
• 6 - Polynomial Regression
• 7 - Support Vector Regression (SVR)
• 8 - Decision Tree Regression
• 9 - Random Forest Regression

3 - Classification

• 14 - Logistic Regression
• 15 - K-Nearest Neighbors (K-NN)
• 16 - Support Vector Machine (SVM)
• 17 - Kernel SVM
• 18 - Naive Bayes
• 19 - Decision Tree Classification
• 20 - Random Forest Classification

4 - Clustering

• 24 - K-Means Clustering
• 25 - Hierarchical Clustering

5 - Association Rule Learning

• 28 - Apriori
• 29 - Eclat

6 - Reinforcement Learning

• 32 - Upper Confidence Bound (UCB)
• 33 - Thompson Sampling

7 - Natural Language Processing

• 36 - Natural Language Processing

8 - Deep Learning

• 39 - Artificial Neural Networks (ANN)
• 40 - Convolutional Neural Networks (CNN)

9 - Dimensionality Reduction

• 43 - Principal Component Analysis (PCA)
• 44 - Linear Discriminant Analysis (LDA)
• 45 - Kernel PCA

10 - Model Selection & Boosting

• 48 - Model Selection
  • K-Fold Cross Validation
  • Grid Search

Usage (No Anaconda)

Install dependencies and create virtual environment using Poetry:

poetry install

Install new dependencies:

poetry add DEPENDENCIES

Refer to the Poetry documentation for further documentation