Intermediate
Machine Learning (scikit-learn)
Classification, regression and model evaluation with scikit-learn; building an end-to-end ML project with your own data.
Request This TrainingWho It's For
Suitable for both academic researchers and corporate teams.
Prerequisites
- The "Data Analysis with Python" course or equivalent experience
Learning Outcomes
- Building classification and regression models
- Train/test split and cross-validation
- Interpreting model performance metrics
- Hyperparameter optimization
Program
1
Introduction to ML
- Supervised/unsupervised learning
- Train-test split
2
Classification
- Decision trees, SVM, logistic regression
3
Regression and Evaluation
- Regression models
- Cross-validation, metrics
4
Model Optimization
- Hyperparameter search
- Preventing overfitting
Tools Used
Python (scikit-learn, pandas)
Frequently Asked Questions
No; this course focuses on classical ML. Deep learning is a separate course.
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