Machine Learning: Classification
Master classification techniques like logistic regression, decision trees & boosting for sentiment analysis & risk prediction.
Master classification techniques like logistic regression, decision trees & boosting for sentiment analysis & risk prediction.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
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Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement logistic regression for large-scale classification tasks
Develop and optimize decision tree models
Master boosting techniques to improve model performance
Handle missing data effectively in real-world scenarios
Evaluate classifiers using precision-recall metrics
Scale machine learning algorithms to huge datasets
Skills you'll gain
This course includes:
8.4 Hours PreRecorded video
19 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 10 modules in this course
This comprehensive course covers essential classification techniques in machine learning. Starting with linear classifiers and logistic regression, students progress through decision trees, boosting, and handling missing data. The curriculum emphasizes practical implementation with case studies in sentiment analysis and loan default prediction. Through hands-on assignments, learners develop skills in model evaluation, preventing overfitting, and scaling algorithms for large datasets.
Welcome
Module 1 · 1 Hours to complete
Linear Classifiers & Logistic Regression
Module 2 · 2 Hours to complete
Learning Linear Classifiers
Module 3 · 2 Hours to complete
Overfitting & Regularization in Logistic Regression
Module 4 · 2 Hours to complete
Decision Trees
Module 5 · 2 Hours to complete
Preventing Overfitting in Decision Trees
Module 6 · 2 Hours to complete
Handling Missing Data
Module 7 · 1 Hours to complete
Boosting
Module 8 · 2 Hours to complete
Precision-Recall
Module 9 · 1 Hours to complete
Scaling to Huge Datasets & Online Learning
Module 10 · 2 Hours to complete
Fee Structure
Instructors
Leader in Machine Learning and Intelligent Applications
Carlos Guestrin is the Amazon Professor of Machine Learning at the University of Washington's Computer Science & Engineering Department. He is also the co-founder and CEO of Dato, Inc., which focuses on facilitating the development of intelligent applications utilizing large-scale machine learning. Prior to his current role, Guestrin served as the Finmeccanica Associate Professor at Carnegie Mellon University and was a senior researcher at Intel Research Lab in Berkeley.
Expert in Machine Learning and Bayesian Modeling
Emily Fox is an assistant professor and the Amazon Professor of Machine Learning in the Statistics Department at the University of Washington. Previously, she was a faculty member in the Wharton Statistics Department at the University of Pennsylvania. Fox has received several prestigious awards, including the Sloan Research Fellowship, a Young Investigator Award from the U.S. Office of Naval Research, and a National Science Foundation CAREER Award.
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4.7 course rating
3,723 ratings
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