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Python With Machine Learning Course
Fungua amaanyi ga Python ne machine learning ku course yaffe eno enjjuvu etungiddwa butereevu eri abakozi abakugu mu tekinologiya. Yingira mu feature engineering, okumanya obulungi scaling, normalization, n'engeri y'okukwatamu categorical variables. Ongera ku bukugu bwo n'enkola z'okutereeza models nga random search ne hyperparameter tuning. Zuula ebifo data we ziva okuva ku UCI ne Kaggle, era ofune obukugu mu algorithms nga random forests ne decision trees. Yimusa obukugu bwo n'okutendekebwa okukola ddala, okwa quality eya waggulu okuteekeddwa okukozesebwa mu bulamu obwa bulijjo.
- Manya bulungi feature engineering: Scale, normalize, era okole features ezisinziira ku budde.
- Tereeza models: Yiga hyperparameter tuning ne grid search techniques.
- Zuula data we ziva obulungi: Kozesa UCI ne Kaggle okufuna datasets ezikwatagana.
- Teekateeka algorithms: Noonyereza ku Random Forests, Decision Trees, ne Linear Regression.
- Pima models: Kozesa MAE, RMSE, n'okugabanya data okwekenneenya engeri gye bikola.
flexible workload from 4 to 360h
certificate recognized by MEC
What will I learn?
Fungua amaanyi ga Python ne machine learning ku course yaffe eno enjjuvu etungiddwa butereevu eri abakozi abakugu mu tekinologiya. Yingira mu feature engineering, okumanya obulungi scaling, normalization, n'engeri y'okukwatamu categorical variables. Ongera ku bukugu bwo n'enkola z'okutereeza models nga random search ne hyperparameter tuning. Zuula ebifo data we ziva okuva ku UCI ne Kaggle, era ofune obukugu mu algorithms nga random forests ne decision trees. Yimusa obukugu bwo n'okutendekebwa okukola ddala, okwa quality eya waggulu okuteekeddwa okukozesebwa mu bulamu obwa bulijjo.
Elevify advantages
Develop skills
- Manya bulungi feature engineering: Scale, normalize, era okole features ezisinziira ku budde.
- Tereeza models: Yiga hyperparameter tuning ne grid search techniques.
- Zuula data we ziva obulungi: Kozesa UCI ne Kaggle okufuna datasets ezikwatagana.
- Teekateeka algorithms: Noonyereza ku Random Forests, Decision Trees, ne Linear Regression.
- Pima models: Kozesa MAE, RMSE, n'okugabanya data okwekenneenya engeri gye bikola.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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