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Python For Machine Learning Course
Bula maatla a Python go ithuta ka botlalo ka makine ka khoso ya rona e e feletseng e e diretsweng bomaitseanape ba thekenoloji. Ithute ka botlalo ka ditsela tsa regression tse di jaaka Random Forests le Decision Trees, itse ditekanyetso tsa go sekaseka modumo tse di jaaka RMSE le MAE, mme o ithute ditsela tsa go baakanya data pele ga e dirisiwa go balela, go akaretsa go lekanya le go tsenya data. Tokafatsa bokgoni jwa gago ka mekgwa ya go tlhopha dilo tse di botlhokwa, mokwalo wa diporojeke, le dilaeborari tsa Python tse di jaaka NumPy le Pandas. Tokafatsa modumo ka hyperparameter tuning le mekgwa ya ensemble. Ikopanye le rona jaanong go oketsa bokgoni jwa gago mo go ithuteng ka botlalo ka makine.
- Itseng regression ka botlalo: Dirisa Random Forests, Decision Trees, le Linear Regression.
- Sekaseka modumo: Dirisa RMSE, MAE, le cross-validation go bona maduo a a leng gone.
- Baakanya data pele ga e dirisiwa go balela: Lekanya dilo tse di botlhokwa, samagana le data e e tlhaelang, mme o tsenye diphetogo.
- Tokafatsa modumo: Dirisa hyperparameter tuning, mekgwa ya ensemble, le maano a go batla.
- Sekaseka data: Dirisa NumPy, Pandas, Matplotlib, le Seaborn go bona lesedi la data.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Bula maatla a Python go ithuta ka botlalo ka makine ka khoso ya rona e e feletseng e e diretsweng bomaitseanape ba thekenoloji. Ithute ka botlalo ka ditsela tsa regression tse di jaaka Random Forests le Decision Trees, itse ditekanyetso tsa go sekaseka modumo tse di jaaka RMSE le MAE, mme o ithute ditsela tsa go baakanya data pele ga e dirisiwa go balela, go akaretsa go lekanya le go tsenya data. Tokafatsa bokgoni jwa gago ka mekgwa ya go tlhopha dilo tse di botlhokwa, mokwalo wa diporojeke, le dilaeborari tsa Python tse di jaaka NumPy le Pandas. Tokafatsa modumo ka hyperparameter tuning le mekgwa ya ensemble. Ikopanye le rona jaanong go oketsa bokgoni jwa gago mo go ithuteng ka botlalo ka makine.
Elevify advantages
Develop skills
- Itseng regression ka botlalo: Dirisa Random Forests, Decision Trees, le Linear Regression.
- Sekaseka modumo: Dirisa RMSE, MAE, le cross-validation go bona maduo a a leng gone.
- Baakanya data pele ga e dirisiwa go balela: Lekanya dilo tse di botlhokwa, samagana le data e e tlhaelang, mme o tsenye diphetogo.
- Tokafatsa modumo: Dirisa hyperparameter tuning, mekgwa ya ensemble, le maano a go batla.
- Sekaseka data: Dirisa NumPy, Pandas, Matplotlib, le Seaborn go bona lesedi la data.
Suggested summary
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