ML lab

https://drive.google.com/file/d/1MGidbOcZ1bN0ENlJiYKGHCPc0H5zfWMP/view?usp=drivesdk https://drive.google.com/file/d/1r1ovdsMNIwelZxvnCw1aXTREt7Az9c-u/view?usp=sharing

This Machine Learning Lab Manual (R22CSM3126) for III Year - II Semester focuses on practical implementation of fundamental machine learning algorithms using Python. The experiments cover classification, clustering, regression, and the workings of neural networks and evolutionary algorithms. Students learn to apply Bayes' rule for probabilistic classification, implement k-nearest neighbors (KNN) for non-parametric classification, and use k-means for clustering. They also gain experience with linear regression and text classification using the Naïve Bayes theorem. More advanced topics include the significance of genetic algorithms and the implementation of the Back-propagation algorithm for training Artificial Neural Networks. The manual also includes additional programs like the FIND-S and Candidate Elimination algorithms for concept learning.

Here are 5 key bullet points of the specific topics covered:

• Bayes' Rule Application: This involves calculating conditional probability, such as finding the probability of a student being absent given that it is Friday, using the formula: $P(C|X)= \frac{P(X|C) \cdot P(C)}{P(X)}$.
• k-Nearest Neighbors (KNN) Classification: This is a non-parametric method used for classification and regression where an object is classified by a majority vote of its neighbors, using a distance metric like Euclidean distance.
• k-Means Clustering: This is an unsupervised learning algorithm used to partition $n$ observations into $k$ clusters, with the objective of minimizing the variance within each cluster. The experiment demonstrates predicting a classification using the result of k-means clustering.
• Linear Regression: A supervised learning algorithm for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data.
• Back-propagation Algorithm: The most widely used algorithm for training Artificial Neural Networks (ANNs). It involves a Forward Pass (propagating inputs to the output layer) and a Backward Pass (propagating the network error back to the input layer to update weights and biases).