- Regression Analysis
- Logistic Regression
- Feature Engineering
- Unsupervised Learning
- Classification Algorithms
- Applied Machine Learning
- Dimensionality Reduction
- Decision Tree Learning
- Predictive Modeling
- Machine Learning
- Scikit Learn (Machine Learning Library)
- Model Evaluation
Machine Learning with Python
Completed by MIRIAM ASTRID GUADALUPE MIRANDA PATIÑO
July 3, 2024
20 hours (approximately)
MIRIAM ASTRID GUADALUPE MIRANDA PATIÑO'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
