- Machine Learning
- Scikit Learn (Machine Learning Library)
- Predictive Modeling
- Dimensionality Reduction
- Logistic Regression
- Classification Algorithms
- Model Evaluation
- Decision Tree Learning
- Applied Machine Learning
- Supervised Learning
- Unsupervised Learning
- Regression Analysis
Machine Learning with Python
Completed by CARLOS ANDRES OSORIO ALCALDE
December 17, 2019
20 hours (approximately)
CARLOS ANDRES OSORIO ALCALDE's account is verified. Coursera certifies their successful completion of Machine Learning with Python
What you will learn
Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.
Skills you will gain
