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Machine Learning Lab Program

 Machine Learning Programs


  1. Implement the matrices operations using both Numpy and pandas 
  2. Using matplotlib to perform data visualization on the standard dataset 
  3. Implement Linear Regression using ordinary least square(OLS) and Gradient Descent methods
  4. Implement quadratic Regression  
  5. Implement Logistic Regression
  6. Evaluate performance measures on regression models (Linear, quadratic and Logistic).
  7. Implement classification using SVM  
  8. Implement Decision-tree learning  
  9. Implement Bagging using Random Forests  
  10. Implement K-means Clustering to Find Natural Patterns in Data  
  11. Implement DBSCAN clustering  
  12. Implement the Gaussian Mixture Model  
  13. Solve the curse of dimensionality by implementing the PCA algorithm on a high-dimensional  
  14. Comparison of Machine Learning algorithms  

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ML Lab Questions

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DBSCAN

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Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probabilistic model used for clustering and density estimation. It assumes that data is generated from a mixture of several Gaussian distributions, each representing a cluster within the dataset. Unlike K-means, which assigns data points to the nearest cluster centroid deterministically, GMM considers each data point as belonging to each cluster with a certain probability, allowing for soft clustering. GMM is ideal when: Clusters have elliptical shapes or different spreads : GMM captures varying shapes and densities, unlike K-means, which assumes clusters are spherical. Soft clustering is preferred : If you want to know the probability of a data point belonging to each cluster (not a hard assignment). Data has overlapping clusters : GMM allows a point to belong partially to multiple clusters, which is helpful when clusters have significant overlap. Applications of GMM Image Segmentation : Used to segment images into regions, where each region can be...
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