Build Regression, Classification, and Clustering Models
Master machine learning algorithms for regression, classification, and clustering with hands-on Python projects. Perfect for intermediate AI practitioners.
Master machine learning algorithms for regression, classification, and clustering with hands-on Python projects. Perfect for intermediate AI practitioners.
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 CertNexus Certified Artificial Intelligence Practitioner Professional Certificate 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.
4.4
(16 ratings)
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What you'll learn
Train and evaluate linear regression models using different approaches
Implement regularization techniques to improve model performance
Build binary and multi-class classification models with various algorithms
Evaluate classification models using confusion matrices and ROC curves
Optimize models through hyperparameter tuning techniques
Develop clustering models to find patterns in unsupervised data
Skills you'll gain
This course includes:
20 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This comprehensive course explores the core machine learning algorithms used to solve supervised and unsupervised learning problems. Students build and evaluate multiple models, starting with linear regression using linear algebra and advancing to regularized and iterative approaches. The curriculum covers binary and multi-class classification, including logistic regression and k-nearest neighbors, with emphasis on model evaluation and hyperparameter tuning. The course culminates with clustering techniques such as k-means and hierarchical clustering. Throughout the program, students apply theoretical concepts through hands-on labs and projects, developing practical skills for selecting and implementing the most appropriate algorithms for various machine learning tasks.
Build Linear Regression Models Using Linear Algebra
Module 1 · 2 Hours to complete
Build Regularized and Iterative Linear Regression Models
Module 2 · 3 Hours to complete
Train Classification Models
Module 3 · 3 Hours to complete
Evaluate and Tune Classification Models
Module 4 · 2 Hours to complete
Build Clustering Models
Module 5 · 3 Hours to complete
Apply What You've Learned
Module 6 · 5 Hours to complete
Fee Structure
Instructor
IBM Watson Innovator Anastas advances AI with ML expertise.
Anastas brings exceptional multidisciplinary expertise to his role developing cutting-edge artificial intelligence and information retrieval technologies at IBM Watson, where he works in close collaboration with IBM Research. His impressive academic background combines an MSc in pure mathematics from Purdue University with a comprehensive BSc from the University of Pittsburgh, encompassing mathematics, computer science, and neuroscience, further enhanced by minors in physics and chemistry. As an IBM-recognized educator and public speaker, he bridges the gap between complex technological innovation and practical application, contributing significantly to IBM Watson's advancement in AI technologies.
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4.4 course rating
16 ratings
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