ANU B.Sc(AI) Machine Learning using Python Lab Manual is now available, the programs provided here are easy to understand. These programs are verified before posting here. There are no errors in the programs.
Course Outcomes :
At the end of this course, the students will be able to:
CO1: Explain the basic concepts of machine learning.
CO2 : Construct supervised learning models.
CO3 : Construct unsupervised learning algorithms.
CO4: Evaluate and compare different models
EXPERIMENT LIST:
1. Write a python program to import and export data using Pandas library functions.
2. Demonstrate various data pre-processing techniques for a given dataset
3. Implement Dimensionality reduction using Principle Component Analysis (PCA) method.
4. Write a Python program to demonstrate various Data Visualization Techniques.
5. Implement Simple and Multiple Linear Regression Models.
6. Develop Logistic Regression Model for a given dataset.
7. Develop Decision Tree Classification model for a given dataset and use it to classify a new sample.
8. Implement Naïve Bayes Classification in Python.
9. Build KNN Classification model for a given dataset.
10. Build Artificial Neural Network model with back propagation on a given dataset.
11. Implement Random forest ensemble method on a given dataset.
12.Implement Boosting ensemble method on a given dataset.
11. Write a python program to implement K-Means clustering Algorithm.
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