Introduction to Machine Learning For Data Science Course
What will I learn?
Vhurai simba re data nechikamu chedu che "Chikamu Chekutanga che Machine Learning ye Data Science", chakagadzirirwa vanamazvikokota ve Business Intelligence. Pindai muzvinhu zvinokosha zvakadai sekucheneswa kwe data, kugadzirwa kwezvinhu zvitsva, nekudzidziswa kwemhando. Batsirai ne algorithms dzepamusoro dzinonzi Random Forests ne Gradient Boosting dzekufembera kutengesa. Dzidzai kunatsiridza mhando kuburikidza nekugadzirisa hyperparameter uye dziongorore uchishandisa zviyero zvakadai se MAE ne RMSE. Simudzai hunyanzvi hwenyu hwe BI ne ruzivo runobatsira, rwemhando yepamusoro runosimudzira budiriro yebhizinesi.
Apoia's Unique Features
Develop skills
Enhance your development of the practical skills listed below
Batsirai nekuchenesa data: Ivai nechokwadi chekururama nekubvisa kusawirirana nezvikanganiso.
Gadzirai kugadzirwa kwezvinhu zvitsva: Gadzirai zvinhu zvine simba kuti mhando ishande zviri nani.
Natidzirai mhando: Wedzerai kururama nekugadzirisa hyperparameter nekuenzanisa algorithm.
Ratidzai ruzivo rwe data: Shandisai maturusi ekuona kuti muwane ruzivo runobatsira.
Ongororai mhando: Yerai budiriro nezviyero zvakadai se MAE ne RMSE.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can modify the chapters and the workload.
- Choose which chapter to start with
- Add or remove chapters
- Increase or decrease the course workload
Examples of chapters you can add
You will be able to generate more chapters like the examples below
This is a free course, focused on personal and professional development. It is not equivalent to a technical, undergraduate, or postgraduate course, but offers practical and relevant knowledge for your professional journey.