Applied Machine Learning in Python
Master practical machine learning implementation using scikit-learn, covering fundamental concepts through advanced model development and optimization.
Master practical machine learning implementation using scikit-learn, covering fundamental concepts through advanced model development and optimization.
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 Applied Data Science with Python 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.
4.6
(8,507 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement machine learning algorithms using scikit-learn
Build and evaluate predictive models for real-world applications
Master feature engineering and model selection techniques
Understand advanced concepts like ensemble methods and neural networks
Apply cross-validation and evaluation metrics effectively
Skills you'll gain
This course includes:
8 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on practical applications of machine learning using Python's scikit-learn library. Students learn essential ML concepts, from basic classification and regression to advanced topics like neural networks and ensemble methods. The curriculum covers supervised and unsupervised learning techniques, feature engineering, model evaluation, and cross-validation. Through hands-on assignments, learners develop skills in implementing various ML algorithms and understanding their real-world applications, while also learning to avoid common pitfalls like data leakage.
Fundamentals of Machine Learning - Intro to SciKit Learn
Module 1 · 6 Hours to complete
Supervised Machine Learning - Part 1
Module 2 · 9 Hours to complete
Evaluation
Module 3 · 6 Hours to complete
Supervised Machine Learning - Part 2
Module 4 · 9 Hours to complete
Fee Structure
Instructor
Associate Professor
Kevyn Collins-Thompson is an Associate Professor of Information and Computer Science in the School of Information at the University of Michigan. His research focuses on developing algorithms and systems to effectively connect people with information, particularly for educational purposes. This work combines applied machine learning, human-computer interaction (HCI), and natural language processing. In addition to his academic roles, he has over a decade of industry experience as a software engineer, manager, and researcher.
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4.6 course rating
8,507 ratings
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