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Machine Learning A-Z
A practical, end-to-end journey through machine learning — from core supervised and unsupervised concepts to hands-on model building and evaluation with scikit-learn.
4 sections·11 lessons
What you'll learn
1. Foundations of Machine Learning
- What is Machine Learning?
- The Machine Learning Landscape
- Data Preparation with scikit-learn
2. Supervised Learning — Regression & Classification
- Linear & Polynomial Regression
- Classification: Logistic Regression, Decision Trees & Random Forests
- Decision Boundaries Visualized
3. Unsupervised Learning & Model Evaluation
- Clustering with K-Means
- Model Evaluation & Cross-Validation
- The Bias-Variance Trade-off
4. Pipelines, Tuning & Real-World Practice
- scikit-learn Pipelines & Hyperparameter Tuning
- Machine Learning Mastery Quiz
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