Text Mining Course

What will I learn?
Ithute go dirisa maatla a tshedimosetso e e mo mokwalong ka Thuto ya Rona ya Go Epolola Tshedimosetso mo Mokwalong, e e diretsweng batho ba ba dirang ka Botlhale jwa Kgwebo (Business Intelligence) ba ba batlang go tokafatsa bokgoni jwa bone jwa go sekaseka. Ithute ka botlalo ditsela tsa go baakanya mokwalo jaaka go khutshwafatsa mafoko (stemming) le go fetola mafoko go ya ka popego ya one (lemmatization), itse go dira dipopego tsa ditlhogo ka Latent Dirichlet Allocation, mme o tlhotlhomise tshekatsheko ya maikutlo o dirisa TextBlob le VADER. Ithute go supa tshedimosetso ka ditsela tse di bonalang jaaka maru a mafoko (word clouds) le dikerafo tsa maikutlo, mme o fetole kitso e e bonweng go nna dikgakololo tse di ka dirisiwang. Godisa bokgoni jwa gago jwa BI ka thuto e khutshwane, e e siameng, e bile e le mosola.
Elevify advantages
Develop skills
- Epolola dintlha tsa botlhokwa: Ithute go bona dintlha tsa botlhokwa mo tshedimosetsong e e mo mokwalong.
- Supa tshedimosetso ka tsela e e bonalang: Ithute go tlhama dipopego tse di kgatlhang tsa tshekatsheko ya mokwalo.
- Baakanya mokwalo: Bona bokgoni jwa go phepafatsa le go tlwaetsa tshedimosetso e e mo mokwalong.
- Dira tshekatsheko ya maikutlo: Sekaseka maikutlo o dirisa TextBlob le VADER.
- Dira dipopego tsa ditlhogo: Aga mme o sekaseke dipopego tsa ditlhogo tse di mosola.
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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