Python for Machine Learning and AI
This course is part of multiple programs. Learn more.
This comprehensive course focuses on practical machine learning and AI implementation using Python. Students learn to build and train decision tree models, explore random forests, and develop sophisticated machine learning solutions. The curriculum covers model training, bias detection, and optimization techniques to prevent underfitting and overfitting. Through real-world case studies and sample datasets, students gain hands-on experience in developing efficient machine learning models for complex decision-making processes.
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What you'll learn
Develop advanced machine learning models using Python
Master decision trees and random forest implementations
Train and optimize models for complex problem-solving
Identify and mitigate bias in machine learning systems
Prepare for advanced data science career opportunities
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This advanced course explores machine learning and artificial intelligence implementation using Python. Students learn to develop sophisticated decision-making models starting with decision trees and progressing to random forests and more complex algorithms. The curriculum emphasizes practical applications using real-world datasets, covering model training, bias detection, and optimization techniques. Students gain hands-on experience in building and evaluating machine learning models while learning to avoid common pitfalls like underfitting and overfitting.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Python for Data Science and Machine Learning, Data Science and Machine Learning
Instructor
9 Courses
Harvard Data Science and Computational Science Expert
Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science (IACS) at Harvard's John A. Paulson School of Engineering and Applied Sciences. With a distinguished career spanning physics, astronomy, and data science, Protopapas has become a leading figure in computational science education and research. He holds a Ph.D. in theoretical physics from the University of Pennsylvania and has focused his recent work on applying machine learning and AI to astronomy and computer science.
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Frequently asked questions
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