Machine Learning Fundamentals with Python
Learn to build predictive models and classify data using supervised and unsupervised learning algorithms through hands-on Python practice.
Learn to build predictive models and classify data using supervised and unsupervised learning algorithms through hands-on Python practice.
This comprehensive course, part of the Data Science MicroMasters program, teaches fundamental machine learning concepts and algorithms. Students learn to implement supervised and unsupervised learning techniques using Python and Jupyter notebooks. Through real-world case studies, participants master classification, regression, ensemble methods, and deep learning. The course covers practical applications like image classification, document analysis, and semantic structure capture, providing hands-on experience in building descriptive and predictive models.
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What you'll learn
Master classification, regression, and probability estimation techniques
Implement generative and discriminative models
Develop expertise in linear models and kernel methods
Master ensemble methods including boosting and random forests
Understand representation learning and deep neural networks
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
The course provides a thorough foundation in machine learning concepts and applications using Python. Students explore both supervised and unsupervised learning techniques, including classification, regression, ensemble methods, and deep learning. Through practical case studies and hands-on programming exercises, participants learn to build and evaluate machine learning models for various real-world applications. The curriculum emphasizes both theoretical understanding and practical implementation skills.
Fee Structure
Instructor
5 Courses
Pioneering Algorithm Theorist and Machine Learning Innovator
Dr. Sanjoy Dasgupta serves as Professor of Computer Science and Engineering at the University of California San Diego, where he has revolutionized the field of algorithmic statistics and unsupervised learning since joining in 2002. Born in Rome and educated in New York City, he earned his A.B. from Harvard in 1993 and Ph.D. from UC Berkeley in 2000, followed by two years at AT&T Labs-Research. His groundbreaking research combines algorithmic theory with geometry and mathematical statistics, focusing on developing efficient algorithms for high-dimensional data analysis and clustering. He is particularly renowned for his work on unsupervised learning, where he explores how machines can learn from their environment with minimal supervision, mimicking human learning processes. His influential textbook "Algorithms," co-authored with Papadimitriou and Vazirani, is widely used in undergraduate courses worldwide. His contributions have earned him significant recognition in the field, particularly for developing the first provably correct, efficient algorithms for various statistical tasks, especially in data clustering and pattern recognition
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