semvec/pypi-packages huggingface dataset:
galo_method_decorators 0.1.0
A library that allows you to use decorators for instance methods without affecting the methods in
Requires Python: >=3.8

# Galo-MethodDecorators

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A Python library that allows you to use decorators for instance methods without affecting the methods in the class.

## Installation

The package is available on the [PyPi](https://pypi.org/project/galo_method_decorators/):

```commandline
pip install galo_method_decorators
```
aiandml 1.0.6
The description of the package
Requires Python: >=3.6

MACHINE LEARNING LABORATORY
[As per Choice Based Credit System (CBCS) scheme] (Effective from the academic year 2018 - 2019) SEMESTER â€“ VII
Subject Code	18CSL76	CIE Marks	40
Course Learning Objectives: This course (18CSL76) will enable students to:
 Implement and evaluate AI and ML algorithms in and Python programming language.
 Descriptions (if any):
Programs List:
1. Implement A* Search algorithm.
  2. Implement AO* Search algorithm.
3. For a given set of training data examples stored in a .CSV file, implement and demonstrate the
Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
4. Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
5. Build an Artificial Neural Network by implementing the Back propagation algorithm and
test the same using appropriate data sets.
6. Write a program to implement the naive Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
7. Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.
8. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this
problem.
9. Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment.