
Mashinaviy oʻqitish algoritmlari so‘nggi yillarda sun’iy intellekt va ma’lumotlarni qayta ishlash sohasida muhim ahamiyat kasb etmoqda. Ushbu maqolada mashinaviy oʻqitish algoritmlarining amaliy qo‘llanilishi, ularning turli sohalardagi samaradorligi va afzalliklari tahlil qilinadi. Shuningdek, nazariy asoslar, mashinaviy oʻqitishning asosiy turlari (nazoratli, nazoratsiz va kuchaytiruvchi oʻqitish), hamda real hayotdagi qo‘llanilish misollari, jumladan, tibbiyotda kasalliklarni aniqlash, moliya sohasida risklarni baholash, sanoatda sifat nazorati va marketing sohasida mijozlar xatti-harakatlarini tahlil qilish kabi amaliy yo‘nalishlar keltiriladi. Maqola shuningdek, mashinaviy oʻqitish algoritmlarini tanlash, optimallashtirish va ulardan samarali foydalanish bo‘yicha tavsiyalar beradi.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
