Embedded Files
Assignment 2 on ML Tools
Submission Date: 30/05/2022
Demo: Recorded video AND Class presentation
Duration of Recorded video: 3 to 5 minutes
Duration of in-class presentation: Less than 10 minutes.
In Assignment 2, everyone is expected to understand and have hands-on experience with at least one ML tool. The submissions are a recorded video for 3 to 5 minutes and a live presentation in the class for not more than 10 minutes, showing the demo of the ML tool that you have explored. Some of the ML tools are listed below. One ML tool can be picked from the list or can be self-chosen after some search. The chosen ML tool should have a pick and place kind of interface.
Some of the ML tools are:
1. Knime
2. Alteryx
3. Tableau + ML Libraries
4. Azure
5. Rapid Miner
6. bigML
7. CloudAutoML
8. H2O driverless AI
9. ML Tool IBM
10. Weka
Assignment 1: Machine Learning Algorithms
Assignment 1: Machine Learning Algorithms
Report Submission Deadline: 27th May
Presentation in Class on 27th May
Machine Learning Algorithms
Each member of the group should learn about one algorithm from the following-
Linear Regression & Logistic Regression & Gradient Descent
Decision Tree
KNN & Naive Bayes
SVM
RandomForest
Explain the algorithm, what are the hyperparameters, how to find optimal values, where the algorithm can be use, advantages and limitations.
Apart from the other sources the ML algorithms can be referred from the website:
https://sites.google.com/view/etiitb-610-2021/
Keep the report brief. 2-3 pages in 11 pt is sufficient.
Page updated
Report abuse