- Model Evaluation
- Applied Machine Learning
- Decision Tree Learning
- Predictive Modeling
- Dimensionality Reduction
- Feature Engineering
- Supervised Learning
- Machine Learning
- Python Programming
- Classification Algorithms
- Scikit Learn (Machine Learning Library)
- Logistic Regression
Machine Learning with Python
Completed by CARLOS RODRIGUEZK CACERES
September 15, 2021
20 hours (approximately)
CARLOS RODRIGUEZK CACERES'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
