Mathematics For Machine Learning Course
What will I learn?
Oketsa bokgoni jwa gago jwa Business Intelligence ka Thuto ya rona ya Mathematics for Machine Learning. Ithute go sekaseka data, o itsenye mo ditseleng tsa go lemoga dilo tse di sa tlwaelegang le go samagana le boleng jo bo tlogetsweng. Ithute go baakanya data pele, go akaretsa go tlwaelanya le go alafa dilo tse di kwa ntle, go tokafatsa bokgoni jwa model. Sekaseka ditsela tsa go ithuta ka machine tsa metseletsele ya nako, jaaka ditlhare tsa ditshwetso le ARIMA. Nna le bokgoni mo go direng dipopego, mekgwa ya go tokafatsa, le tshekatsheko ya model. Thuto e e go thusa ka bokgoni jo bo mosola, jwa maemo a a kwa godimo mo ditirong tsa mmatota.
Apoia's Unique Advantages
Develop skills
Enhance the development of the practical skills listed below
Itseng bokgoni jwa go rulaganya data: Sekaseka le go tlhaloganya ditsela tse di raraganeng tsa data ka bokgoni.
Lemoga dilo tse di sa tlwaelegang: Lemoga dipharologanyo go tokafatsa boammaaruri le go tshepega ga data.
Dirisa mekgwa ya metseletsele ya nako: Dirisa ARIMA le LSTM go dira ditshupo tse di nepahetseng.
Tokafatsa ditsela: Dirisa gradient descent go thapisa model ka bokgoni.
Dira dipopego: Tlhama dipopego tsa polynomial go tokafatsa tiragatso ya model.
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’ll 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.