- Dimensionality Reduction
- Feature Engineering
- Regression Analysis
- Scikit Learn (Machine Learning Library)
- Predictive Modeling
- Supervised Learning
- Logistic Regression
- Decision Tree Learning
- Python Programming
- Model Evaluation
- Unsupervised Learning
- Classification Algorithms
Machine Learning with Python
Completed by Ricardo Yoshitaka Ikeda
August 18, 2020
20 hours (approximately)
Ricardo Yoshitaka Ikeda'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
