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Python For Machine Learning Course
Notlolla matla a Python bakeng sa thuto ea mochini ka thupelo ea rona e akaretsang e etselitsoeng litsebi tsa theknoloji. Kena ka har'a li-algorithm tsa regression tse kang Random Forests le Decision Trees, tseba litekanyetso tsa tlhahlobo ea mohlala joalo ka RMSE le MAE, 'me u hlahlobe mekhoa ea ho lokisa data e kenyelletsang ho lekanya likarolo le ho kenya khoutu. Ntlafatsa tsebo ea hau ka mekhoa ea khetho ea likarolo, litokomane tsa morero, le lilaebrari tsa Python tse kang NumPy le Pandas. Ntlafatsa mehlala ka ho fetola li-hyperparameter le mekhoa ea kopano. Kena hona joale ho phahamisa boiphihlelo ba hau thutong ea mochini.
- Tseba regression: Kenyelletsa Random Forests, Decision Trees, le Linear Regression.
- Hlahloba mehlala: Sebelisa RMSE, MAE, le cross-validation bakeng sa litekanyetso tsa ts'ebetso.
- Lokisa data: Leka likarolo, sebetsana le data e sieo, 'me u kenye mefuta e fapaneng.
- Ntlafatsa mehlala: Sebelisa hyperparameter tuning, mekhoa ea kopano, le maqheka a ho batla.
- Sekaseka data: Sebelisa NumPy, Pandas, Matplotlib, le Seaborn bakeng sa lintlha tsa data.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Notlolla matla a Python bakeng sa thuto ea mochini ka thupelo ea rona e akaretsang e etselitsoeng litsebi tsa theknoloji. Kena ka har'a li-algorithm tsa regression tse kang Random Forests le Decision Trees, tseba litekanyetso tsa tlhahlobo ea mohlala joalo ka RMSE le MAE, 'me u hlahlobe mekhoa ea ho lokisa data e kenyelletsang ho lekanya likarolo le ho kenya khoutu. Ntlafatsa tsebo ea hau ka mekhoa ea khetho ea likarolo, litokomane tsa morero, le lilaebrari tsa Python tse kang NumPy le Pandas. Ntlafatsa mehlala ka ho fetola li-hyperparameter le mekhoa ea kopano. Kena hona joale ho phahamisa boiphihlelo ba hau thutong ea mochini.
Elevify advantages
Develop skills
- Tseba regression: Kenyelletsa Random Forests, Decision Trees, le Linear Regression.
- Hlahloba mehlala: Sebelisa RMSE, MAE, le cross-validation bakeng sa litekanyetso tsa ts'ebetso.
- Lokisa data: Leka likarolo, sebetsana le data e sieo, 'me u kenye mefuta e fapaneng.
- Ntlafatsa mehlala: Sebelisa hyperparameter tuning, mekhoa ea kopano, le maqheka a ho batla.
- Sekaseka data: Sebelisa NumPy, Pandas, Matplotlib, le Seaborn bakeng sa lintlha tsa data.
Suggested summary
Before starting, you can change the chapters and the 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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