Machine Learning projects
Decision Tree From Scratch -> Understanding decision trees algorithm, the concept of false branch and True branch where each node has following properties:
-> Id ; where parent node has id n , the false branch child has id 2n+1 and the true branch child has id 2n+2.
-> A decision node will have question
-> A leaf node will have max class label counts or prediction class
-> depth - depth of the tree
-> Perceptron Neural Nets Lab
-> Using Neural net to solve the XOR gate problem
-> Creating a neural net to solve a classification problem using Wine dataset from sklearn to understand the machine learning project lifecyle
-> KMeans from Scratch
-> Clustering tweets by utilizing Jaccard Distance metric and K-means clustering algorithm.
Objectives:
1. Compute the similarity between tweets using the Jaccard Distance metric.
2. Cluster tweets using the K-means clustering algorithm.
Note= K initial centroids that have been tested to generate good results.