Mathematics For Machine Learning Course
What will I learn?
Gattako obumanyirivu bwo mu Business Intelligence n'ekyo course yaffe eya Mathematics for Machine Learning. Tambula mu kunoonyereza ku data, okumanya amagezi ag'okuzuula ebitagenda bulungi (outliers) n'okukwataganya ebitaliwo (missing values). Yiga okuteekateeka data, nga mw'otwalidde okugigolola (normalization) n'okujjanjaba ebitagenda bulungi, okwongera obutuufu bwa model. Kunoonyereza ku nkola za machine learning ezikwata ku bintu ebikyuka mu biseera (time series), nga decision trees ne ARIMA. Funa obumanyirivu mu kukola features, amagezi ag'okulongoosa, n'okukebera model. Course eno ekuwa obumanyirivu obulina omugaso, obw'omutindo ogwa waggulu obw'okukozesa mu bulamu obwa bulijjo.
Apoia's Unique Features
Develop skills
Strengthen the development of the practical skills listed below
Yiga okukozesa data structures: Noola era onnyonnyole data sets enzibu mu ngeri entuufu.
Zuula ebitagenda bulungi (outliers): Zuula ebikyamu okwongera obutuufu bwa data n'okugeesiga.
Kozesa time series models: Kozesa ARIMA ne LSTM okubala ebinaabaawo mu butuufu.
Longoose algorithms: Kozesa gradient descent okutendeka model mu ngeri ennungi.
Kola features: Kola polynomial features okwongera ku nkola ya model.
Suggested summary
Workload: between 4 and 360 hours
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
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.