Classification Methods: Problems and Solutions
Course Features
Duration
6 hours
Delivery Method
Online
Available on
Limited Access
Accessibility
Mobile, Desktop, Laptop
Language
English
Subtitles
English
Level
Beginner
Teaching Type
Self Paced
Video Content
6 hours
Course Description
Course Overview
International Faculty
Post Course Interactions
Instructor-Moderated Discussions
Skills You Will Gain
Prerequisites/Requirements
Fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
To get the most out of this course, you should have familiarity with programming in a Python development environment
What You Will Learn
Demonstrate proficiency in other ensemble methods for classification.
Differentiate between the uses and applications of classification and classification ensembles.
Implement a variety of error metrics to compare the efficiency of various classification models to choose the one that suits your data the best.
Use decision tree and tree-ensemble models.
Utilize logistic regression, KNN, and SVM models.
Target Students
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.
Course Content
Course Instructors
Fateme Akbari
Data Scientist @IBM
Roodra Kanwar
Data Scientist at IBM
