- Unsupervised Learning
- Dimensionality Reduction
- Model Evaluation
- Predictive Modeling
- Applied Machine Learning
- Decision Tree Learning
- Regression Analysis
- Machine Learning
- Classification Algorithms
- Scikit Learn (Machine Learning Library)
- Logistic Regression
- Feature Engineering
Machine Learning with Python
Completed by Jairo Andrés Hernández Mosquera
November 20, 2019
20 hours (approximately)
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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
