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INT395 Units

Practice

SUPERVISED LEARNING

INT395 3 Credits L:2 T:0 P:2 Minor AI / ML

This course focuses on employability and skill development in the field of supervised learning. It covers data preprocessing, standard classification algorithms, ensemble learning, regression techniques, time series forecasting, and model evaluation strategies.

Study Units

Unit 1

Introduction and Data Preprocessing

Notes MCQ
Unit 2

Classification with scikit-learn

Notes MCQ
Unit 3

Ensemble Methods and Hyperparameter Tuning

Notes MCQ
Unit 4

Regression with scikit-Learn

Notes MCQ
Unit 5

Time Series Regression

Notes MCQ
Unit 6

Pipelines, Model Evaluation and Model Deployment

Notes MCQ

Continuous Assessment

Project 50%
Test - Code based 50%

Project

50%

Each student will be assigned a topic for a project. Students will be evaluated based upon the innovation of the solution, presentation skills and report writing.

Week Allottment: Week 3 / Submission: Week 11

Rubric: 1. Report / Documentation -10, 2. Execution / Implementation -10, 3. Presentation / Viva - 10

Test - Code based

50%

To make students understand and implement the concepts of machine learning. This task will contain topics from unit 1 and unit 2.

Week Allottment: Week 5 / Submission: Week 6

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%

All MCQ

Coming Soon

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

MCQ + Subjective

Mock Test

Quick Navigation for INT395

Unit 1 Notes Unit 2 Notes Unit 3 Notes Unit 4 Notes Unit 5 Notes Mock Test

INT395 - FAQs

How many units are in INT395?

INT395 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for INT395?

Available resources include: ETE Mock Test.

How to prepare for INT395 exams?

Study each unit's notes thoroughly, practice MCQs to test understanding, and attempt mock tests before exams. Focus on important topics and previous year questions.