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Machine Learning Course
Vhurai simba re mashine kudzidza nechidzidzo chedu chakadzama chakagadzirirwa nyanzvi dzemagetsi. Nyururirai mukutora nekutsvaga data muchishandisa Pandas ne NumPy, ganhurai hunyanzvi hwekupatsanura dataset, uye ongororai mamodels nehungwaru. Dzidzai nzira dzinokosha dzekugadzirira data, tsvakai nzira dzepamusoro dzekusarudza model, uye muwane nzwisiso mumamodels ekudzokera kumashure akadai seDecision Trees neRandom Forests. Simbisai hunyanzvi hwenyu nezvinyorwa zvinoshanda uye zvemhando yepamusoro zvinoita kuti mugadzirire kutarisana nematambudziko chaiwo enyika. Joinai zvino mushandure basa renyu!
- Iva nyanzvi mukubata data: Rodzai, ongororai, uye sarudzai ma dataset ne Pandas ne NumPy.
- Shandisai kupatsanurwa kwema dataset: Shandisai cross-validation uye stratified sampling techniques.
- Ongororai mashandiro emamodel: Nzwisisai MAE, MSE, uye R-squared metrics.
- Gadzirirai data nemazvo: Batirai nekushaikwa kwemavara uye encode categorical variables.
- Simbisai mamodels: Tune hyperparameters uye shandisai ensemble methods.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Vhurai simba re mashine kudzidza nechidzidzo chedu chakadzama chakagadzirirwa nyanzvi dzemagetsi. Nyururirai mukutora nekutsvaga data muchishandisa Pandas ne NumPy, ganhurai hunyanzvi hwekupatsanura dataset, uye ongororai mamodels nehungwaru. Dzidzai nzira dzinokosha dzekugadzirira data, tsvakai nzira dzepamusoro dzekusarudza model, uye muwane nzwisiso mumamodels ekudzokera kumashure akadai seDecision Trees neRandom Forests. Simbisai hunyanzvi hwenyu nezvinyorwa zvinoshanda uye zvemhando yepamusoro zvinoita kuti mugadzirire kutarisana nematambudziko chaiwo enyika. Joinai zvino mushandure basa renyu!
Elevify advantages
Develop skills
- Iva nyanzvi mukubata data: Rodzai, ongororai, uye sarudzai ma dataset ne Pandas ne NumPy.
- Shandisai kupatsanurwa kwema dataset: Shandisai cross-validation uye stratified sampling techniques.
- Ongororai mashandiro emamodel: Nzwisisai MAE, MSE, uye R-squared metrics.
- Gadzirirai data nemazvo: Batirai nekushaikwa kwemavara uye encode categorical variables.
- Simbisai mamodels: Tune hyperparameters uye shandisai ensemble methods.
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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