Mathematics For Machine Learning Course
What will I learn?
Ntlafatsa tsebo ea hao ea Business Intelligence ka Mathematics for Machine Learning Course ea rona. Ikenye ka botebo ho hlahlobeng data, u itšebellise ka mekhoa ea ho lemoha tse sa tloaelehang le ho sebetsana le boleng bo sieo. Ithute ho lokisa data, ho kenyeletsa ho tloaetsa le kalafo e sa tloaelehang, ho ntlafatsa nepo ea mohlala. Ithute ka li-algorithm tsa ho ithuta mochini bakeng sa letoto la linako, joalo ka lifate tsa liqeto le ARIMA. Fumana boiphihlelo bo tebileng ho boenjiniere ba likarolo, mekhoa ea optimization le tlhahlobo ea mohlala. Koetliso ena e u fa tsebo e sebetsang, ea boleng bo holimo bakeng sa ts'ebeliso ea 'nete ea lefatše.
Apoia's Unique Features
Develop skills
Enhance your practical skills as listed below
Tseba lisebelisoa tsa data: Sekaseka le ho hlalosa li-data tse rarahaneng ka nepo.
Lebella tse sa tloaelehang: Tseba lintho tse sa tloaelehang ho ntlafatsa nepo ea data le ho tšepahala.
Sebelisa mefuta ea letoto la linako: Sebelisa ARIMA le LSTM bakeng sa ponelopele e nepahetseng.
Ntlafatsa li-algorithms: Sebelisa gradient descent bakeng sa koetliso e sebetsang ea mohlala.
Etsa likarolo tsa boenjiniere: Theha likarolo tsa polynomial ho ntlafatsa ts'ebetso ea mohlala.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can adjust the chapters and workload.
- Choose your starting chapter
- Add or remove chapters
- Alter the total course workload
Examples of chapters you can include
You'll be able to generate additional chapters similar to 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 provides practical and relevant knowledge for your career.