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  • ืคืชืจื•ื ื•ืช
    ืžื—ืฉื‘ื•ืŸ ืื™ื ื˜ื’ืจืœื™ื ืžื—ืฉื‘ื•ืŸ ื ื’ื–ืจืช ืžื—ืฉื‘ื•ืŸ ืืœื’ื‘ืจื” ืžื—ืฉื‘ื•ืŸ ืžื˜ืจื™ืฆื•ืช ื™ื•ืชืจ...
  • ื’ืจืคื™ื
    ื’ืจืฃ ืงื•ื•ื™ื ื’ืจืฃ ืืงืกืคื•ื ื ืฆื™ืืœื™ ื’ืจืฃ ืจื™ื‘ื•ืขื™ ื’ืจืฃ ืกื™ื ื•ืก ื™ื•ืชืจ...
  • ืžื—ืฉื‘ื•ื ื™ื
    ืžื—ืฉื‘ื•ืŸ BMI ืžื—ืฉื‘ื•ืŸ ืจื™ื‘ื™ืช ื“ืจื™ื‘ื™ืช ืžื—ืฉื‘ื•ืŸ ืื—ื•ื–ื™ื ืžื—ืฉื‘ื•ืŸ ื”ืืฆื” ื™ื•ืชืจ...
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    ืžื—ืฉื‘ื•ืŸ ืžืฉืคื˜ ืคื™ืชื’ื•ืจืก ืžื—ืฉื‘ื•ืŸ ืฉื˜ื— ืžืขื’ืœ ืžื—ืฉื‘ื•ืŸ ืžืฉื•ืœืฉ ืฉื•ื•ื” ืฉื•ืงื™ื™ื ืžื—ืฉื‘ื•ืŸ ืžืฉื•ืœืฉื™ื ื™ื•ืชืจ...
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    ืžื—ื‘ืจืช ืงื‘ื•ืฆื•ืช ืฉืœื™ืคื™ื ื“ืคื™ ืขื‘ื•ื“ื” ืžื“ืจื™ื›ื™ ืœื™ืžื•ื“ ืชืจื’ื•ืœ ืืžืช ืคืชืจื•ืŸ
    • he
    English Español Português Français Deutsch Italiano ะ ัƒััะบะธะน ไธญๆ–‡(็ฎ€ไฝ“) ํ•œ๊ตญ์–ด ๆ—ฅๆœฌ่ชž Tiแบฟng Viแป‡t ืขื‘ืจื™ืช ุงู„ุนุฑุจูŠุฉ
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Practice ืชืชืืžืŸ ื™ื•ืชืจ
ื”ืงืœื“ ืืช ืชืฉื•ื‘ืชืš
x^2 x^{\msquare} \log_{\msquare} \sqrt{\square} \nthroot[\msquare]{\square} \le \ge \frac{\msquare}{\msquare} \cdot \div x^{\circ} \pi
\left(\square\right)^{'} \frac{d}{dx} \frac{\partial}{\partial x} \int \int_{\msquare}^{\msquare} \lim \sum \infty \theta (f\:\circ\:g) f(x)
โ–ญ\:\longdivision{โ–ญ} \times \twostack{โ–ญ}{โ–ญ} + \twostack{โ–ญ}{โ–ญ} - \twostack{โ–ญ}{โ–ญ} \left( \right) \times \square\frac{\square}{\square}
ืงื— ืืชื’ืจ
ื”ื™ืจืฉื ื›ื“ื™ ืœืืžืช ืืช ืชืฉื•ื‘ืชืš
ืจื›ื•ืฉ ืžื ื•ื™
?ื”ืื ืืชื” ื‘ื˜ื•ื— ืฉืืชื” ืจื•ืฆื” ืœืขื–ื•ื‘ ืืช ื”ืืชื’ืจ ื”ื–ื” ืขืœ ื™ื“ื™ ืกื’ื™ืจืช ื—ืœื•ืŸ ื–ื” ืชืื‘ื“ ืืช ื”ืืชื’ืจ ื”ื–ื”
ื‘ื™ื˜ื•ืœ
ืฆื
  • ื˜ืจื•ื ืืœื’ื‘ืจื”
    ืกื“ืจ ืคืขื•ืœื•ืช ื—ืฉื‘ื•ืŸ ื’ื•ืจืžื™ื ืžืฉื•ืชืคื™ื ื•ืจืืฉื•ื ื™ื™ื ืฉื‘ืจื™ื ื—ื™ื‘ื•ืจ, ื—ื™ืกื•ืจ, ื›ืคืœ, ื—ื™ืœื•ืง ืืจื•ืš ืžืกืคืจื™ื ืขืฉืจื•ื ื™ื™ื ื—ื–ืงื•ืช ื•ืฉื•ืจืฉื™ื ื™ื—ืก ื•ืคืจื•ืคื•ืจืฆื™ื•ืช ืžื•ื“ื•ืœื• ืžืžื•ืฆืข, ื—ืฆื™ื•ืŸ, ืฉื›ื™ื—
  • ืืœื’ื‘ืจื”
    ืžืฉื•ื•ืื•ืช ืื™ ืฉื™ื•ื•ื™ื•ื ื™ื ืžืขืจื›ืช ืžืฉื•ื•ืื•ืช ืžืขืจื›ืช ืื™ ืฉื•ื•ื™ื•ื ื™ื ืคืขื•ืœื•ืช ื‘ืกื™ืกื™ื•ืช ืชื›ื•ื ื•ืช ืืœื’ื‘ืจื™ื•ืช ืคืจื•ืง ืœืฉื‘ืจื™ื ื—ืœืงื™ื™ื ืคื•ืœื™ื ื•ืžื™ื ื‘ื™ื˜ื•ื™ื™ื ืจืฆื™ื•ื ืœื™ื™ื
  • ื˜ืจื•ื ื—ืฉื‘ื•ืŸ ืื™ื ืคื™ื˜ื™ืกืžืœื™
    ืžืฉื•ื•ืื•ืช ืื™ ืฉื™ื•ื•ื™ื•ื ื™ื ืžืกืคืจื™ื ืžืจื•ื›ื‘ื™ื ืงื•ืจื“ื™ื ื˜ื•ืช ืงื•ื˜ื‘ื™ื•ืช / ืคื•ืœืจื™ื•ืช ืžืขืจื›ืช ืื™ ืฉื•ื•ื™ื•ื ื™ื ืคื•ืœื™ื ื•ืžื™ื ืจืฆื™ื•ื ืœื™ื ืคื•ื ืงืฆื™ื•ืช ืืจื™ืชืžื˜ื™ืงื” ื•ื”ืจื›ื‘ื” ื’ืื•ืžื˜ืจื™ื” ืื ืœื™ื˜ื™ืช ื˜ืจื™ื’ื•ื ื•ืžื˜ืจื™ื”
  • ื—ืฉื‘ื•ืŸ ืื™ื ืคื™ื˜ืกื™ืžืœื™
    ื ื’ื–ืจื•ืช ืฉื™ืžื•ืฉื™ ื”ื ื’ื–ืจืช ื’ื‘ื•ืœื•ืช ืื™ื ื˜ื’ืจืœื™ื ืฉื™ืžื•ืฉื™ ืื™ื ื˜ื’ืจืœื™ื ื˜ื•ืจื™ื ืžืฉื•ื•ืื•ืช ื“ื™ืคืจื ืฆื™ืืœื™ื•ืช ืจื’ื™ืœื•ืช ื˜ืจื ืกืคื•ืจืžืฆื™ืช ืœืคืœืก ื˜ื•ืจื™ ื˜ื™ื™ืœื•ืจ / ืžืงืœื•ืจืŸ
  • ืคื•ื ืงืฆื™ื•ืช
    ืžืฉื•ื•ืืช ื”ื™ืฉืจ ืคื•ื ืงืฆื™ื•ืช ืืจื™ืชืžื˜ื™ืงื” ื•ื”ืจื›ื‘ื” ื—ืชื›ื™ ื—ืจื•ื˜
  • ืืœื’ื‘ืจื” ืœื™ื ืืจื™ืช
    ืžื˜ืจื™ืฆื•ืช ื•ืงื˜ื•ืจื™ื
  • ื˜ืจื™ื’ื•ื ื•ืžื˜ืจื™ื”
    ื–ื”ื•ื™ื•ืช ื”ื•ื›ื—ืช ื–ื”ื•ื™ื•ืช ืžืฉื•ื•ืื•ืช ื˜ืจื™ื’ื•ื ื•ืžื˜ืจื™ื•ืช ื”ืขืจื›ืช ืคื•ื ืงืฆื™ื•ืช ืคื™ืฉื•ื˜
  • ืกื˜ื˜ื™ืกื˜ื™ืงื”
    ืžืžื•ืฆืข ื—ืฉื‘ื•ื ื™ ืžืžื•ืฆืข ื”ื ื“ืกื™ ืžืžื•ืฆืข ืจื™ื‘ื•ืขื™ ื—ืฆื™ื•ืŸ ืฉื›ื™ื— ืกื“ืจ ืžื™ื ื™ืžื•ื ืžืงืกื™ืžื•ื ื”ืกืชื‘ืจื•ืช ืืžืฆืข ื”ื˜ื•ื•ื— ื˜ื•ื•ื— ืกื˜ื™ื™ืช ืชืงืŸ ืฉื•ื ื•ืช ืจื‘ืขื•ืŸ ืชื—ืชื•ืŸ ืจื‘ืขื•ืŸ ืขืœื™ื•ืŸ ื˜ื•ื•ื— ื‘ื™ืŸ ืจื™ื‘ืขื•ื ื™ ืžื“ื“ ืžื™ืงื•ื

ื”ืฆืขื” ืžื™ื•ื—ื“ืช

ื›ืคืœื™ื™ื ื”ื›ืœื™ื, ืžื—ื™ืจ ืžืฉืชืœื ืื—ื“

ืœืฉืœื•ื˜ ื‘ืžืชืžื˜ื™ืงื” ื•ื‘ื›ืชื™ื‘ื” ืฉืœืš
Pro
  • ื’ื™ืฉื” ืžืœืื” ืœืฉืœื‘ื™ ื”ืคืชืจื•ืŸ
  • ื’ื™ืฉื” ืžืœืื” ืœืฆ'ืื˜ ืฉืœ ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช
  • ืชืจื’ื•ืœ ื•ืฉื™ืคื•ืจ
  • ื’ื™ืฉื” ืžื›ืœ ืžื›ืฉื™ืจ
  • ืื™ืŸ ืคืจืกื•ืžื•ืช
  • ืื—ืกื•ืŸ ื‘ืœืชื™ ืžื•ื’ื‘ืœ
  • ื–ื™ื”ื•ื™ ืชื•ื›ืŸ ืฉื ื•ืฆืจ ืขืœ ื™ื“ื™ ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช
  • ื‘ื•ื“ืง ื“ืงื“ื•ืง ืžืชืงื“ื
  • ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ืื ื•ืฉื™ืช
  • ืคืจืคืจื–ื” ื‘ืžืฆื‘ื™ื ื‘ืœืชื™ ืžื•ื’ื‘ืœื™ื
  • ืกื™ื›ื•ื ื›ืœ ื˜ืงืกื˜
  • ื”ืจื‘ื” ื™ื•ืชืจ
ื—ืœื™ื ื”ื’ื‘ืœื•ืช, ืขื™ื™ื ื• ื‘ืฉืืœื•ืช ื”ื ืคื•ืฆื•ืช ืฉืœื ื•.

ืงื™ื“ื•ื ื—ื–ืจื” ืœื‘ื™ืช ื”ืกืคืจ

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ื ื•ืฉื
  • ื˜ืจื•ื ืืœื’ื‘ืจื”
  • ืืœื’ื‘ืจื”
  • ื˜ืจื•ื ื—ืฉื‘ื•ืŸ ืื™ื ืคื™ื˜ื™ืกืžืœื™
  • ื—ืฉื‘ื•ืŸ ืื™ื ืคื™ื˜ืกื™ืžืœื™
  • ืคื•ื ืงืฆื™ื•ืช
  • ืืœื’ื‘ืจื” ืœื™ื ืืจื™ืช
  • ื˜ืจื™ื’ื•ื ื•ืžื˜ืจื™ื”
  • ืกื˜ื˜ื™ืกื˜ื™ืงื”
ืงื‘ืœ ืืช ื”ืชื•ืกืฃ ืฉืœื ื•, ืืชื” ื™ื›ื•ืœ ืœืชืคื•ืก ื›ืœ ื‘ืขื™ื” ื‘ืžืชืžื˜ื™ืงื” ืžื›ืœ ืืชืจ ืื™ื ื˜ืจื ื˜
ืžืงืœื“ืช ืžืœืื”
x^2 x^{\msquare} \log_{\msquare} \sqrt{\square} \nthroot[\msquare]{\square} \le \ge \frac{\msquare}{\msquare} \cdot \div x^{\circ} \pi
\left(\square\right)^{'} \frac{d}{dx} \frac{\partial}{\partial x} \int \int_{\msquare}^{\msquare} \lim \sum \infty \theta (f\:\circ\:g) f(x)
- \twostack{โ–ญ}{โ–ญ} \lt 7 8 9 \div AC
+ \twostack{โ–ญ}{โ–ญ} \gt 4 5 6 \times \square\frac{\square}{\square}
\times \twostack{โ–ญ}{โ–ญ} \left( 1 2 3 - x
โ–ญ\:\longdivision{โ–ญ} \right) . 0 = + y
\mathrm{ืคื™ืฉื•ื˜} \mathrm{ื‘ื™ื“ื•ื“\:ืžืฉืชื ื”} \mathrm{ืคื•ื ืงืฆื™ื”\:ื”ื•ืคื›ื™ืช} \mathrm{ืžืฉื™ืง} \mathrm{ื™ืฉืจ}
ืจืื” ื”ื›ืœ
ืฉื˜ื—
ืืกื™ืžืคื˜ื•ื˜ื•ืช
ื ืงื•ื“ื•ืช ื—ืฉื•ื“ื•ืช
ื ื’ื–ืจืช
ืชื—ื•ื ื”ื’ื“ืจื”
ืขืจืš ืขืฆืžื™
ื•ืงื˜ื•ืจ ืขืฆืžื™
ืคืชื™ื—ืช ืกื•ื’ืจื™ื™ื
ื ืงื•ื“ื•ืช ืงื™ืฆื•ืŸ
ืคื™ืจื•ืง ืœื’ื•ืจืžื™ื
ื ื’ื–ืจืช ืฉืœ ืคื•ื ืงืฆื™ื” ืกืชื•ืžื”
ื ืงื•ื“ื•ืช ืคื™ืชื•ืœ
ื ืงื•ื“ื•ืช ื—ื™ืชื•ืš
ืคื•ื ืงืฆื™ื” ื”ื•ืคื›ื™ืช
ื˜ืจื ืกืคื•ืจืžืฆื™ืช ืœืคืœืก
ื˜ืจื ืกืคื•ืจืžืฆื™ืช ืœืคืœืก ื”ืคื•ื›ื”
ืคืจื•ืง ืœืฉื‘ืจื™ื ื—ืœืงื™ื™ื
ืชืžื•ื ื”
ืฉื™ืคื•ืข
ืคื™ืฉื•ื˜
ื‘ื™ื“ื•ื“ ืžืฉืชื ื”
ืžืฉื™ืง
ื˜ื•ืจื™ ื˜ื™ื™ืœื•ืจ
ืงื•ื“ืงื•ื“ื™ื
geometric test
alternating test
telescoping test
pseries test
root test
ืฆืขื“ื™ื ื’ืจืฃ ืงืฉื•ืจ ื“ื•ื’ืžืื•ืช
ื ื•ืฆืจ ืขืœ ื™ื“ื™ AI
ื”ืกื‘ืจื™ AI ื ื•ืฆืจื™ื ื‘ืืžืฆืขื•ืช ื˜ื›ื ื•ืœื•ื’ื™ื™ืช OpenAI. ืชื•ื›ืŸ ืฉื ื•ืฆืจ ืขืœ ื™ื“ื™ AI ืขืฉื•ื™ ืœื”ืฆื™ื’ ืชื•ื›ืŸ ืœื ืžื“ื•ื™ืง ืื• ืคื•ื’ืขื ื™ ืฉืื™ื ื• ืžื™ื™ืฆื’ ืืช ื”ืฉืงืคืชื” ืฉืœ Symbolab.
ืืžืช ืืช ืชืฉื•ื‘ืชืš
ื”ื™ืจืฉื ื›ื“ื™ ืœืืžืช ืืช ืชืฉื•ื‘ืชืš
ืจื›ื•ืฉ ืžื ื•ื™
!ืฉืžื•ืจ ืœืžื—ื‘ืจืช
ื”ืชื—ื‘ืจ ื›ื“ื™ ืœืฉืžื•ืจ ื”ืขืจื•ืช
ื”ืชื—ื‘ืจ
 
ืื™ืžื•ืช
ืฉืžื•ืจ
ื”ืจืื” ื“ืจืš
 
ื”ืกืชืจ ื“ืจืš
 

Number Line

ืงืฉื•ืจ
ื“ื•ื’ืžืื•ืช
  • x^{2}-x-6=0
  • -x+3\gt 2x+1
  • line\:(1,\:2),\:(3,\:1)
  • f(x)=x^3
  • prove\:\tan^2(x)-\sin^2(x)=\tan^2(x)\sin^2(x)
  • \frac{d}{dx}(\frac{3x+9}{2-x})
  • (\sin^2(\theta))'
  • \sin(120)
  • \lim _{x\to 0}(x\ln (x))
  • \int e^x\cos (x)dx
  • \int_{0}^{\pi}\sin(x)dx
  • \sum_{n=0}^{\infty}\frac{3}{2^n}
ืชื™ืื•ืจ
ืžื—ืฉื‘ ืืœื’ื‘ืจื”, ื˜ืจื™ื’ื•ื ื•ืžื˜ืจื™ื” ื•ื—ืฉื‘ื•ืŸ ื“ื™ืคืจื ืฆื™ืืœื™ ื•ืื™ื ื˜ื’ืจืืœื™ ืฆืขื“ ืื—ืจ ืฆืขื“

step-by-step

Khor20CallF0๏ฟฝ๏ฟฝ๏ฟฝ+971552434396๐Ÿ’Ž20Girls20Fakkan

he

ืคื•ืกื˜ื™ื ืงืฉื•ืจื™ื ื‘ื‘ืœื•ื’ ืฉืœ Symbolab
  • Practice Makes Perfect
    Learning math takes practice, lots of practice. Just like running, it takes practice and dedication. If you want...
    • ืฆ'ืื˜ ืขื ืกื™ืžื‘ื• AI
    ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ืขืฉื•ื™ื” ืœื”ืฆื™ื’ ืชื•ื›ืŸ ืœื ืžื“ื•ื™ืง ืื• ืคื•ื’ืขื ื™ ืฉืื™ื ื• ืžื™ื™ืฆื’ ืืช ื”ื“ืขื•ืช ืฉืœ Symbolab.
    ืื™ืŸ ืœื”ื–ื™ืŸ ืžื™ื“ืข ืื™ืฉื™
    ื”ื›ื ืก ื‘ืขื™ื”
    !ืฉืžื•ืจ ืœืžื—ื‘ืจืช
    ื”ืชื—ื‘ืจ
    ืžื—ื‘ืจืช
      ืฆืคื” ื‘ืžื—ื‘ืจืช ื”ืžืœืื”
      ื›ืœื™ ืœื™ืžื•ื“ ืคื•ืชืจ ืžืชืžื˜ื™ืงื” ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช AI Chat ื“ืคื™ ืขื‘ื•ื“ื” ืชืจื’ื•ืœ ืฉืœื™ืคื™ื ืžื—ืฉื‘ื•ื ื™ื ืžื—ืฉื‘ื•ืŸ ื’ืจืคื™ ืžื—ืฉื‘ื•ืŸ ื’ืื•ืžื˜ืจื™ื” ืืžืช ืคืชืจื•ืŸ
      ืืคืœื™ืงืฆื™ื•ืช ืืคืœื™ืงืฆื™ื™ืช Symbolab (Android) ืžื—ืฉื‘ื•ืŸ ื’ืจืคื™ (Android) ืชืจื’ื•ืœ (Android) ืืคืœื™ืงืฆื™ื™ืช Symbolab (iOS) ืžื—ืฉื‘ื•ืŸ ื’ืจืคื™ (iOS) ืชืจื’ื•ืœ (iOS) ืชื•ืกืฃ Chrome
      ื—ึถื‘ืจึธื” ืขืœ Symbolab ื‘ืœื•ื’ ืขื–ืจื” ืฆื•ืจ ืงืฉืจ
      ืžืฉืคื˜ื™ ืคืจื˜ื™ื•ืช Service Terms ืžื“ื™ื ื™ื•ืช ืงื•ื‘ืฆื™ Cookie ื”ื’ื“ืจื•ืช ืขื•ื’ื™ื•ืช ื–ื›ื•ื™ื•ืช ื™ื•ืฆืจื™ื, ื”ื ื—ื™ื•ืช ืงื”ื™ืœื”, DSA ื•ืžืฉืื‘ื™ื ืžืฉืคื˜ื™ื™ื ืื—ืจื™ื ืžืจื›ื– ืžืฉืคื˜ื™ Learneo
      ืžึธืฉืื•ึนื‘ ืžื“ื™ื” ื—ื‘ืจืชื™ืช
      Symbolab, a Learneo, Inc. business
      © Learneo, Inc. 2024
      (ืื•ืคืฆื™ื•ื ืืœื™)
      (ืื•ืคืฆื™ื•ื ืืœื™)

      ื”ื•ืกืฃ ื”ื•ื“ืขื”.

      ื”ื•ื“ืขื” ื”ืชืงื‘ืœื”. ืชื•ื“ื” ืขืœ ื”ืžืฉื•ื‘.

      ื‘ื™ื˜ื•ืœ ืฉืœื—