https://github.com/benjaminpla/machine_learning

Machine Learning course by Digital House academy
https://github.com/benjaminpla/machine_learning

git machine-learning ml python

Last synced: 9 months ago
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Machine Learning course by Digital House academy

• Host: GitHub
• URL: https://github.com/benjaminpla/machine_learning
• Owner: benjaminPla
• Created: 2024-07-04T19:48:16.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-07-07T13:51:37.000Z (over 1 year ago)
• Last Synced: 2025-02-06T20:01:02.904Z (11 months ago)
• Topics: git, machine-learning, ml, python
• Language: Python
• Homepage:
• Size: 4.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine Learning

## Supervised Learning

You have the output column and you train the model with it.

- **Classification**: Predict a class label or category (e.g., true/false, spam/ham, cat/dog).

  - Logistic Regression (binary or multi-class)

  - Decision Tree

  - Random Forest

  - Support Vector Machine (SVM)

  - K-Nearest Neighbors (KNN)

  - Naive Bayes

  - Gradient Boosting Machines (GBM)

  - Neural Networks

- **Regression**: Predict a continuous value (e.g., price, temperature, age).

  - Linear Regression

  - Decision Tree Regression

  - Random Forest Regression

  - Support Vector Regression (SVR)

  - K-Nearest Neighbors Regression

  - Ridge Regression

  - Lasso Regression

  - Polynomial Regression

  - Neural Networks (e.g., Multilayer Perceptron)

## Unsupervised Learning

You don't have the output column and you train the model without it.

- **Dimensionality Reduction**: Reduce the number of features while preserving important information.

  - Principal Component Analysis (PCA)

  - t-Distributed Stochastic Neighbor Embedding (t-SNE)

  - Singular Value Decomposition (SVD)

  - Linear Discriminant Analysis (LDA)

  - Independent Component Analysis (ICA)

- **Density Estimation**: Estimate the probability distribution of data.

  - Gaussian Mixture Models (GMM)

  - Kernel Density Estimation (KDE)

- **Market Basket Analysis**: Identify associations between items.

  - Apriori Algorithm

  - Eclat Algorithm

- **Clustering**: Group similar data points together.

  - K-Means

  - Hierarchical Clustering

  - DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

  - Mean Shift

# Metrics

## Summary:

- **Accuracy** gives an overall measure of correct predictions.

- **Confusion Matrix** breaks down the types of correct and incorrect predictions.

- **Recall** (or sensitivity) focuses on correctly identifying positive cases.

- **Specificity** (or true negative rate) focuses on correctly identifying negative cases.

- **F1 Score** balances precision and recall into a single metric, useful when there's an uneven class distribution.