scikit-learn Fundamentals
Build production machine learning pipelines with Python's premier ML library.
Course overview
scikit-learn is the industry-standard library for classical machine learning in Python. This course focuses on writing clean, reproducible ML code using the scikit-learn API: custom pipelines, feature preprocessors, imputation, scaling, one-hot encoding, model selection, and scoring.
Prerequisites
What you'll achieve
- Master the consistent fit/transform/predict API pattern in scikit-learn
- Construct robust, leakage-free data pipelines with Pipeline and ColumnTransformer
- Tune model hyperparameters with cross-validation workflows
- Evaluate models with detailed classification and regression metric reports
Curriculum
- 1The scikit-learn design philosophy: Estimators, Predictors & Transformers
- 2Preprocessing with StandardScaler, MinMaxScaler, OneHotEncoder & SimpleImputer
- 3Building unified pipelines with make_pipeline and ColumnTransformer
- 4Training regression models (LinearRegression, Ridge, Lasso, RandomForestRegressor)
- 5Training classification models (LogisticRegression, SVC, RandomForestClassifier)
- 6Model evaluation with cross_val_score and classification_report
- 7Hyperparameter optimization with GridSearchCV and RandomizedSearchCV
- 8Project: Production-ready scikit-learn pipeline for tabular prediction
Who is this for?
Data analysts and Python programmers wanting structured, reusable machine learning pipelines.
Contact admissions for current fee and available learning formats
Enrol in scikit-learn Fundamentals
- 5 weeks
- Intermediate
- Live online · One-to-one available · Group batches when scheduled
- 1-on-1 & small batches
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Ready to start your learning journey?
Apply today and our admissions team will review your application and guide you through the next steps.