Python For Machine Learning Course
Vhura simba re Python re machine learning ne course yedu yakadzama yakagadzirirwa nyanzvi dze tekinoroji. Dzika mukati me regression algorithms se Random Forests ne Decision Trees, iva nyanzvi mumayero ekuyera model se RMSE ne MAE, uye ongorora nzira dzekugadzirira data kusanganisira feature scaling ne encoding. Simudzira hunyanzvi hwako nenzira dzekusarudza feature, magwaro e project, uye maraibhurari ePython se NumPy ne Pandas. Gadzirisa mamodels ne hyperparameter tuning nenzira dze ensemble. Joinha izvozvi kuti ukwidze hunyanzvi hwako mu machine learning.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Vhura simba re Python re machine learning ne course yedu yakadzama yakagadzirirwa nyanzvi dze tekinoroji. Dzika mukati me regression algorithms se Random Forests ne Decision Trees, iva nyanzvi mumayero ekuyera model se RMSE ne MAE, uye ongorora nzira dzekugadzirira data kusanganisira feature scaling ne encoding. Simudzira hunyanzvi hwako nenzira dzekusarudza feature, magwaro e project, uye maraibhurari ePython se NumPy ne Pandas. Gadzirisa mamodels ne hyperparameter tuning nenzira dze ensemble. Joinha izvozvi kuti ukwidze hunyanzvi hwako mu machine learning.
Elevify advantages
Develop skills
- Iva nyanzvi mu regression: Shandisa Random Forests, Decision Trees, uye Linear Regression.
- Yera mamodels: Shandisa RMSE, MAE, uye cross-validation yezviyero zve performance.
- Gadzirira data: Scale features, bata data risingapo, uye encode categorical variables.
- Gadzirisa mamodels: Shandisa hyperparameter tuning, ensemble methods, uye nzira dzekutsvaga.
- Ongorora data: Shandisa NumPy, Pandas, Matplotlib, uye Seaborn kuti uwane nzwisiso yedata.
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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