Data Science va sun'iy intellekt
Noldan Data Scientist / ML muhandisigacha — ma'lumotni tozalash, statistika, machine learning, chuqur o'rganish va LLM. Sof o'zbek tilida, real misollar bilan.
29 bo'lim · 348 dars
- 1.1-dars: Data Science nima19 daq
- 1.2-dars: Muhit va vositalar17 daq
- 1.3-dars: Ma'lumot turlari va tuzilishi18 daq
- 1.4-dars: Data Science ish oqimi (CRISP-DM)18 daq
- 1.5-dars: Statistik fikrlash asoslari18 daq
- 1.6-dars: Birinchi tahlil (mini loyiha)17 daq
- 1.7-dars: Data Science etikasi18 daq
- 1.8-dars: Karyera va yo'l xaritasi18 daq
- 2.1-dars: NumPy nima va massiv (ndarray)19 daq
- 2.2-dars: Massiv yaratish18 daq
- 2.3-dars: Atributlar va turlar18 daq
- 2.4-dars: Indekslash va slicing18 daq
- 2.5-dars: Vektorlashtirilgan amallar19 daq
- 2.6-dars: Broadcasting18 daq
- 2.7-dars: Shakl o'zgartirish (reshape)17 daq
- 2.8-dars: Agregatsiya (sum, mean, axis)17 daq
- 2.9-dars: Shart va tanlash19 daq
- 2.10-dars: Tasodifiy sonlar17 daq
- 2.11-dars: Chiziqli algebra19 daq
- 2.12-dars: Tezlik (sikl vs vektor)18 daq
- 2.13-dars: Saqlash va tuzoqlar (copy vs view)19 daq
- 2.14-dars: NumPy amaliy loyiha18 daq
- 3.1-dars: Pandas nima va Series17 daq
- 3.2-dars: DataFrame17 daq
- 3.3-dars: O'qish va yozish (CSV/Excel)17 daq
- 3.4-dars: Tanlash (loc va iloc)17 daq
- 3.5-dars: Filtrlash17 daq
- 3.6-dars: Ustunlar bilan ishlash17 daq
- 3.7-dars: Yo'q qiymatlar (NaN)18 daq
- 3.8-dars: Guruhlash (groupby)16 daq
- 3.9-dars: Birlashtirish (merge, concat)18 daq
- 3.10-dars: Saralash va tartiblash16 daq
- 3.11-dars: Apply va transformatsiya18 daq
- 3.12-dars: Vaqt qatorlari (datetime)17 daq
- 3.13-dars: Pivot va reshape17 daq
- 3.14-dars: Pandas amaliy loyiha17 daq
- 4.1-dars: Statistika nima17 daq
- 4.2-dars: Markazlashuv (o'rtacha, median, mod)17 daq
- 4.3-dars: Tarqoqlik (dispersiya, std, diapazon)17 daq
- 4.4-dars: Kvartillar va persentillar17 daq
- 4.5-dars: Taqsimotlar17 daq
- 4.6-dars: Normal taqsimot18 daq
- 4.7-dars: Markaziy limit teoremasi18 daq
- 4.8-dars: Namuna olish17 daq
- 4.9-dars: Korrelyatsiya17 daq
- 4.10-dars: Korrelyatsiya vs sababiyat18 daq
- 4.11-dars: Ehtimollik asoslari17 daq
- 4.12-dars: Gipoteza tekshirish18 daq
- 4.13-dars: t-test va ishonch oralig'i18 daq
- 4.14-dars: Statistik amaliy loyiha18 daq
- 5.1-dars: Vizualizatsiya va Matplotlib16 daq
- 5.2-dars: Chiziqli grafik16 daq
- 5.3-dars: Ustunli grafik16 daq
- 5.4-dars: Nuqtali grafik (scatter)17 daq
- 5.5-dars: Histogram16 daq
- 5.6-dars: Box plot18 daq
- 5.7-dars: Seaborn17 daq
- 5.8-dars: Heatmap (issiqlik xaritasi)18 daq
- 5.9-dars: Taqsimot grafiklari (KDE, violin)18 daq
- 5.10-dars: Ko'p grafik (subplots)18 daq
- 5.11-dars: Rang, uslub va bezash18 daq
- 5.12-dars: Yaxshi grafik tamoyillari19 daq
- 5.13-dars: Yolg'on grafiklar (aldamaslik)19 daq
- 5.14-dars: Vizualizatsiya amaliy loyiha20 daq
- 6.1-dars: Yetishmayotgan qiymatlar18 daq
- 6.2-dars: Dublikatlar17 daq
- 6.3-dars: Outlier (chetki qiymatlar)18 daq
- 6.4-dars: Tur o'zgartirish17 daq
- 6.5-dars: Normalizatsiya18 daq
- 6.6-dars: Standartlashtirish17 daq
- 6.7-dars: Matn tozalash17 daq
- 6.8-dars: Sana tozalash17 daq
- 6.9-dars: Kategoriya kodlash18 daq
- 6.10-dars: Noto'g'ri qiymatlar19 daq
- 6.11-dars: Tozalash quvuri (pipeline)18 daq
- 6.12-dars: Amaliyot (to'liq tozalash loyihasi)18 daq