- Applied Machine Learning
- Supervised Learning
- Scikit Learn (Machine Learning Library)
- Logistic Regression
- Classification Algorithms
- Predictive Modeling
- Model Evaluation
- Machine Learning
- Unsupervised Learning
- Python Programming
- Decision Tree Learning
- Regression Analysis
Machine Learning with Python
Completed by Adrián Jiménez Benítez
April 8, 2024
20 hours (approximately)
Adrián Jiménez Benítez'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
