Applied Machine Learning Course
What will I learn?
ናይ Business Intelligence ክእለትካ/ክእለትኪ ኣብዚ ትግበራ ዝተገበረ ማሽን ምምሃር ኮርስ ብምስታፍ ኣመሓይሽ/ኣመሓይሺ። እዚ ኮርስ'ዚ ነቶም ግቡእ ዝኾነን ጥዑምን ትምህርቲ ንምርካብ ዝደልዩ ሰብ ሞያ ተባሂሉ ዝተዳለወ እዩ። ከመይ ጌርካ data ምምዳብ ከም ትኽእል/ትኽእሊ ተምሃር/ተምሃሪ፡ ናይ feature engineering ኣድህቦትካ/ኣድህቦትኪ ኣስፍሕ/ኣስፍሒ፡ ከምኡ'ውን ናይ ሞዴል ምምራጽ ብቴክኒክታት ከም decision treesን ensemble methodsን መርምር/መርምሪ። ኣብ ሞዴል ምልምማድ፡ ገምጋምን ምምሕያሽን ዘለካ/ዘለኪ ተውህቦ ኣመሓይሽ/ኣመሓይሺ፡ ከምኡ'ውን ተግባራዊ ዝኾነ ምርዳእን ምኽርን ኣማዕብል/ኣማዕብሊ። እዚ ኮርስ'ዚ data ናብ ስትራተጂካዊ ውሳኔታት ንምቕያር የኽእለካ/የኽእለኪ፡ ኩሉ'ውን በቲ ንዓኻ/ንዓኺ ዝጥዕም ፍጥነት ክትገብሮ/ክትገብርዮ ትኽእል/ትኽእሊ ኢኻ/ኢኺ።
Apoia's Unique Features
Develop skills
Enhance your practical skills outlined below
ን data ምምዳብ ምልላይ: Normalize ግበር/ግበሪ፡ ዘየለ data ተቆጻጸር/ተቆጻጸሪ፡ ከምኡ'ውን ተለዋወጥቲ encoding ግበር/ግበሪ።
ሞዴላት ኣመሓይሽ/ኣመሓይሺ: Hyperparameters ኣስተኻኽል/ኣስተኻኽሊ፡ ከምኡ'ውን ንዝበለጸ ኣፈጻጽማ algorithms ኣነጻጽር/ኣነጻጽሪ።
ሞዴላት ገምግም/ገምግሚ: ናይ ሞዴል ልክዕነት ንምግምጋምን ንምምሕያሽን መለክዒታት ተጠቐም/ተጠቐሚ።
ምርዳእ ኣማዕብል/ኣማዕብሊ: ተግባራዊ ዝኾነ ናይ ስራሕ ንግዲ ምክሪ ንምሃብ ውጽኢት ተንትን/ተንትኒ።
Feature ምህንድስና: ትንቢታዊ ሓይሊ ንምዕባይ ሓደስቲ ናይ data ባህርያት ፍጠር/ፍጠሪ።
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can change chapters and 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.