- Regression Analysis
- Feature Engineering
- Unsupervised Learning
- Scikit Learn (Machine Learning Library)
- Decision Tree Learning
- Predictive Modeling
- Supervised Learning
- Dimensionality Reduction
- Model Evaluation
- Classification Algorithms
- Logistic Regression
- Applied Machine Learning
Machine Learning with Python
Completed by JOAQUIN ALEJANDRO ALIAGA FREYTES
July 26, 2022
20 hours (approximately)
JOAQUIN ALEJANDRO ALIAGA FREYTES'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
