Guls h an  K um ar	
Mach in e l e a rn in g  e n g in eer	
9103091562	 	g u ls h an ku m ar1 00g @ gm ail .c o m	 	lin ke d in .c o m /in /g u ls h an 100	 	Ja m mu, I n d ia	 	
g it h ub.c o m /g u ls h an -1 00	 	
E D U CAT IO N	
Bach elo r 	o f 	E n gin eerin g 	in  	C om pute r 	S cie n ce	
U niv e rs it y  	o f 	J a m mu	
12/2 020 &ndash;  0 7/2 024	 |  J a m mu,	 I n d ia	
I N TER N SH IP S	
M ach in e 	Le a rn in g 	In te rn	
T a le n flo w
06/2 024 &ndash;  p re se n t	
&bull;D eve lo p ed  a  c h atb ot u sin g N LP  a n d  R AG	 	
te ch n iq ues f o r r e sp on siv e  q uery  r e so lu tio n  o n  t h e	 	
co m pan y&#039;s  w eb sit e .
&bull;U til iz e d  O pen AI A P I, P yth on , a n d  P in eco n e D B.	
D ata  	S cie n ce  	In te rn	
Z id io  	D eve lo p m en t	
02/2 024 &ndash;  0 3/2 024	
&bull;C re a te d  a n d  e va lu ate d  p re d ic tiv e  a n aly tic s a n d	 	
patt e rn  r e co gn it io n  m od els .
&bull;Im ple m en te d  f e a tu re  e n gin eerin g, c la ssif ic a tio n ,	 	
an d  r e g re ssio n  f o r p erfo rm an ce  o ptim iz a tio n .	
P R O JE C TS	
AI 	P ow ere d  	C alo rie  	B urn  	P re d ic tio n  	an d  	
M an age m en t 	T o ol	
05/2 024 &ndash;  0 6/2 024	
&bull;D eve lo p ed  a  F la sk  w eb  a p p u sin g X G Boost t o	 	
p re d ic t e xe rc is e  c a lo rie  b urn  f r o m  a  K aggle	 	
d ata se t.
&bull;In te g ra te d  a  u se r-fr ie n d ly  i n te rfa ce  w it h  B oots tra p	 	
fo r r e a l- t im e p re d ic tio n s.	
P la n t 	D is e a se  	P re d ic tio n  	usin g 	C on vo lu tio n  	
N eu ra l 	N etw ork s	
04/2 024 &ndash;  0 5/2 024	
&bull;D esig n ed  a  C on vo lu tio n al N eu ra l N etw ork  ( C N N)	 	
m od el t o  c la ssif y  p la n t d is e a se s f r o m  l e a f i m age s.
&bull;Im ple m en te d  a  w eb  a p plic a tio n  f o r i n sta n t d is e a se	 	
d ia gn osis  u sin g u p lo ad ed  i m age s.	
A I 	p ow ere d  	ch atb ot 	fo r 	T a le n flo w  	sta rtu p	
06/2 024 &ndash;  p re se n t	
&bull;B uil d in g a n  A I- d riv e n  c h atb ot w it h  R AG  a n d	 	
O pen AI A P I t o  s tre a m lin e c u sto m er i n q uir ie s.
&bull;E n han cin g u se r e xp erie n ce  t h ro u gh  i n tu it iv e ,	 	
a u to m ate d  r e sp on se s a n d  r e a l- t im e a ssis ta n ce .	
S K IL LS	
D ata  s c ie n ce , M ach in e l e a rn in g a n d  D eep  l e a rn in g
Pyth on , S u p erv is e d  a n d  u n su p erv is e d  a lg o rit h m s,	 	
N eu ra l N etw ork s, A N N, C N N, R N N, L S T M , F e a tu re	 	
e n gin eerin g, F e a tu re  s e le ctio n , N atu ra l L a n gu age	 	
P ro ce ssin g e tc .
P yth on  P ack age s a n d  F ra m ew ork s
Scik it -le a rn , T e n so rflo w , K era s, N um py, P an d as,	 	
M atp lo tlib , N LT K , O pen C V
La n gu age s
H TM L, C SS , J a va sc rip t, J q uery , N od e.js , F la sk
D ata B ase s
S Q L, P o stg re S Q L
Gen era tiv e  A I
G PT, R AG , G em in i,  P in eco n e, H uggin g F a ce ,	 	
La n gch ain
M LO PS
G CP, G it , D ock e r, S tre a m lit	
C ER TIF IC AT IO NS	
Mach in e l e a rn in g s p ecia liz a tio n
D eep  l e a rn in g s p ecia liz a tio n
La n gch ain  f o r L LM  A p plic a tio n  d eve lo p m en t
La n gch ain  -  C hat w it h  y o u r d ata  ( d eep le a rn in g.a i)
P ro m pt e n gin eerin g f o r d eve lo p ers
S Q L f o r D ata  A n aly sis  -  I n te rn sh ala	
O RG AN IZ AT IO NS	
Cheg g 	In d ia	
S u b je ct 	M atte r 	E xp ert	
0 5/2 023 &ndash;  p re se n t	
&bull;Part t im e S u b je ct M att e r E xp ert a t C heg g,	 	
p ro fic ie n tly  a d dre sse d  o ve r 3 20 p ro bab il it y  a n d	 	
sta tis tic s q uestio n s.	
G eek sfo rG eek s
Te ch n ic a l 	w rit e r	
0 1/2 023 &ndash;  0 5/2 023	
&bull;C on tr ib ute d  2 3 c le a r a n d  r e a d er-fr ie n d ly  t e ch n ic a l	 	
artic le s t o  G FG , r e a ch in g o ve r 1 00,0 00 r e a d ers .