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Was getting queries on this, yes even as students you can deploy your projects on Google Cloud โฌ๏ธ
If you work or want to work on Google Cloud, they are offering 300 USD free for all for a limited period of time, so go ahead and grab the same asap.
Link here : https://ad.doubleclick.net/ddm/trackclk/N5295.4347315TECHGIG/B28999036.394199262;dc_trk_aid=585472451;dc_trk_cid=183313769;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;ltd=;dc_tdv=1
This is officially by Google Cloud and you can run,test and deploy on GCP free for 300 USD ๐ซก
If you work or want to work on Google Cloud, they are offering 300 USD free for all for a limited period of time, so go ahead and grab the same asap.
Link here : https://ad.doubleclick.net/ddm/trackclk/N5295.4347315TECHGIG/B28999036.394199262;dc_trk_aid=585472451;dc_trk_cid=183313769;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;ltd=;dc_tdv=1
This is officially by Google Cloud and you can run,test and deploy on GCP free for 300 USD ๐ซก
Google Cloud
Free Trial and Free Tier Services and Products
Start building on Google Cloud with $300 in free credits and free usage of 20+ products like Compute Engine and Cloud Storage, up to monthly limits.
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Blackrock is hiring Business Analysts
Experience: 0 - 3 year's
Apply here: https://BlackRock.contacthr.com/139438032
Experience: 0 - 3 year's
Apply here: https://BlackRock.contacthr.com/139438032
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Register for Hackathon DevX JMI - https://unstop.com/hackathons/devx-jmi-jamia-millia-islamia-1055794?lb=Uy5zoiXM&utm_medium=Share&utm_source=shortUrl
A good opportunity to build some good projects for your resume!
A good opportunity to build some good projects for your resume!
Unstop
Participate in DevX JMI & win exciting prizes. | 2024 | 1055794 // Unstop
Find out the best DevX JMI that match your interests. Prove your mettle and win exciting prizes like job opportunities and cash rewards from leading companies. | 2024
class UserMainCode(object):
@classmethod
def theLastChocolate(cls, input1, input2):
dp = [[0] * (input1 + 1) for _ in range(input1 + 1)]
dp[1][1] = 1
for i in range(2, input1 + 1):
for j in range(1, i + 1):
dp[i][j] += dp[i - 1][j - 1] if j - 1 >= 1 else 0
dp[i][j] += dp[i - 1][j] if j <= i - 1 else 0
return dp[input1][input2].
// Amazon ml THE LAST Chocolate
@classmethod
def theLastChocolate(cls, input1, input2):
dp = [[0] * (input1 + 1) for _ in range(input1 + 1)]
dp[1][1] = 1
for i in range(2, input1 + 1):
for j in range(1, i + 1):
dp[i][j] += dp[i - 1][j - 1] if j - 1 >= 1 else 0
dp[i][j] += dp[i - 1][j] if j <= i - 1 else 0
return dp[input1][input2].
// Amazon ml THE LAST Chocolate
๐๐ฆ ๐๐น๐ด๐ผ ๐ป ๐ ใ๐๐ผ๐บ๐ฝ๐ฒ๐๐ถ๐๐ถ๐๐ฒ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ดใ
class UserMainCode(object): @classmethod def theLastChocolate(cls, input1, input2): dp = [[0] * (input1 + 1) for _ in range(input1 + 1)] dp[1][1] = 1 for i in range(2, input1 + 1): for j in range(1, i +โฆ
int gcd(int a, int b) {
while (b != 0) {
int t = b;
b = a % b;
a = t;
}
return a;
}
int calculatesun(int input1, int input2[]) {
// Read only region end
int N = input1;
int max_so_far = input2[0];
for (int i = 0; i < N; ++i) {
max_so_far = std::max(max_so_far, input2[i]);
input2[i] = gcd(input2[i], max_so_far);
}
std::sort(input2, input2 + N);
int sum = 0;
for (int i = 0; i < N / 2; ++i) {
sum += gcd(input2[i], input2[N - 1 - i]);
}
return sum;
}
Sum in Array โ
Amazon ML
while (b != 0) {
int t = b;
b = a % b;
a = t;
}
return a;
}
int calculatesun(int input1, int input2[]) {
// Read only region end
int N = input1;
int max_so_far = input2[0];
for (int i = 0; i < N; ++i) {
max_so_far = std::max(max_so_far, input2[i]);
input2[i] = gcd(input2[i], max_so_far);
}
std::sort(input2, input2 + N);
int sum = 0;
for (int i = 0; i < N / 2; ++i) {
sum += gcd(input2[i], input2[N - 1 - i]);
}
return sum;
}
Sum in Array โ
Amazon ML
Random forest Reg
0.0923
K Nearest Neighbours algorithm. By identifying the similarity in physical features of each herb, it can be classified as a given species.
0.0032, computer = "yes"
K-Means Clustering
import pandas as pd
file = pd.read_csv('salary.csv')
q = [0,.25,.50,.75,1]
label = ['0 to 25', '25 to 50', '50 to 75', '75 to 1']
file['Age_q'] = pd.qcut(file['Age'], q = q, labels = label)
Factor of 4
c1 = (data['Grade'] == 'A') & (data['Marks'] > 60)
c2 = (data['Grade'] == 'B') & (data['Marks'] > 70)
c3 = (data['Grade'] == 'C') & (data['Marks'] > 80)
c = pd.Series(np.select([c1,c2,c3], ['Yes','Yes','Yes'], default='No'), index=data.index)
data['Admission'] = data['Admission'].fillna(c)\
From options (0.371,0.971,0.241.0) => correct => 0 โ
Question=> (Indian,student, male) => answer 1.5 โ
Linear regression question =>high bais, low variance,underfitting of data
Amazon ML MCQ โ
0.0923
K Nearest Neighbours algorithm. By identifying the similarity in physical features of each herb, it can be classified as a given species.
0.0032, computer = "yes"
K-Means Clustering
import pandas as pd
file = pd.read_csv('salary.csv')
q = [0,.25,.50,.75,1]
label = ['0 to 25', '25 to 50', '50 to 75', '75 to 1']
file['Age_q'] = pd.qcut(file['Age'], q = q, labels = label)
Factor of 4
c1 = (data['Grade'] == 'A') & (data['Marks'] > 60)
c2 = (data['Grade'] == 'B') & (data['Marks'] > 70)
c3 = (data['Grade'] == 'C') & (data['Marks'] > 80)
c = pd.Series(np.select([c1,c2,c3], ['Yes','Yes','Yes'], default='No'), index=data.index)
data['Admission'] = data['Admission'].fillna(c)\
From options (0.371,0.971,0.241.0) => correct => 0 โ
Question=> (Indian,student, male) => answer 1.5 โ
Linear regression question =>high bais, low variance,underfitting of data
Amazon ML MCQ โ
def min_key_presses(input1):
S=input1
n = len(S)
i = 0
key_presses = 0
while i < n:
if S[i] == '0':
zero_count = 0
while i < n and S[i] == '0':
zero_count += 1
i += 1
key_presses += zero_count // 2
if zero_count % 2 == 1:
key_presses += 1
else:
while i < n and S[i] != '0':
key_presses += 1
i += 1
return key_presses
Amazon MLโ
S=input1
n = len(S)
i = 0
key_presses = 0
while i < n:
if S[i] == '0':
zero_count = 0
while i < n and S[i] == '0':
zero_count += 1
i += 1
key_presses += zero_count // 2
if zero_count % 2 == 1:
key_presses += 1
else:
while i < n and S[i] != '0':
key_presses += 1
i += 1
return key_presses
Amazon MLโ
Matrix value of x 3
Alphabet count 9:45
F'(x) 1/1+x^4 A
James Simon 1/20
John peter tom 1/4
Equal no of chocolates 7
Exams 86
Students weights 60.2
G(3) 13
Perfect sq 1/3
P -1
Restaurant tables 10
3.45 bits
it learns based on unlabbled data
only D
b and d
Alphabet count 9:45
F'(x) 1/1+x^4 A
James Simon 1/20
John peter tom 1/4
Equal no of chocolates 7
Exams 86
Students weights 60.2
G(3) 13
Perfect sq 1/3
P -1
Restaurant tables 10
3.45 bits
it learns based on unlabbled data
only D
b and d
t1 t2 relation t1>t2
Trigrams c
Age income student comp 1.0
Ek c1 = (data['Grade'] == 'A') & (data['Marks'] > 60)
c2 = (data['Grade'] == 'B') & (data['Marks'] > 70)
c3 = (data['Grade'] == 'C') & (data['Marks'] > 80)
c = pd.Series(np.select([c1, c2, c3], ['Yes', 'Yes', 'Yes'], default='No'), index=data.index)
data['Admission'] = data['Admission'].fillna(c)
Reinforcement learing scenario all A, B, C, D
Yactual ypredicted iska B
Nouns and parts C
Perceptron model 0
Amazon MLโ
Trigrams c
Age income student comp 1.0
Ek c1 = (data['Grade'] == 'A') & (data['Marks'] > 60)
c2 = (data['Grade'] == 'B') & (data['Marks'] > 70)
c3 = (data['Grade'] == 'C') & (data['Marks'] > 80)
c = pd.Series(np.select([c1, c2, c3], ['Yes', 'Yes', 'Yes'], default='No'), index=data.index)
data['Admission'] = data['Admission'].fillna(c)
Reinforcement learing scenario all A, B, C, D
Yactual ypredicted iska B
Nouns and parts C
Perceptron model 0
Amazon MLโ
int solve(int idx, int n, int countZero, vector<vector<int>>& dp) {
int mod=1e4+7;
if (idx >= n) {
if (countZero >= 2)
return 1;
return 0;
}
if (dp[idx][countZero] != -1)
return dp[idx][countZero];
int count = 0;
for (int i = 0; i <= 9; ++i) {
if (idx == 0 && i == 0)
continue;
if (i == 0)
count = (count + solve(idx + 1, n, countZero + 1, dp)) % mod;
else
count = (count + solve(idx + 1, n, countZero, dp)) % mod;
}
return dp[idx][countZero] = count;
}
int bounty(int input1) {
vector<vector<int>> dp(input1 + 1, vector<int>(input1 + 1, -1));
if (input1 < 2)
return 0;
if (input1 == 2)
return 1;
return solve(0, input1, 0, dp);
}
Bounty
Amazon MLโ
int mod=1e4+7;
if (idx >= n) {
if (countZero >= 2)
return 1;
return 0;
}
if (dp[idx][countZero] != -1)
return dp[idx][countZero];
int count = 0;
for (int i = 0; i <= 9; ++i) {
if (idx == 0 && i == 0)
continue;
if (i == 0)
count = (count + solve(idx + 1, n, countZero + 1, dp)) % mod;
else
count = (count + solve(idx + 1, n, countZero, dp)) % mod;
}
return dp[idx][countZero] = count;
}
int bounty(int input1) {
vector<vector<int>> dp(input1 + 1, vector<int>(input1 + 1, -1));
if (input1 < 2)
return 0;
if (input1 == 2)
return 1;
return solve(0, input1, 0, dp);
}
Bounty
Amazon MLโ
John sam 0.88
8 red balls 0.35
Differential b
Alphabet mean 9.45
2 digit number 12
r nonzero k=1
Mean median mode range A
max diff 162
G(3) =13
AA' x=2
profit margin 0.1667
Red 2nd ball black 1st ball 8/13
Apti Answers
Amazon MLโ
8 red balls 0.35
Differential b
Alphabet mean 9.45
2 digit number 12
r nonzero k=1
Mean median mode range A
max diff 162
G(3) =13
AA' x=2
profit margin 0.1667
Red 2nd ball black 1st ball 8/13
Apti Answers
Amazon MLโ
class Compute:
@classmethod
def compute(cls, input1, input2):
n = input1
arr = input2
result = []
for i in range(n):
m = 0
k = n - 2
left = i - 1
right = i + 1
if i == 0:
for i in range(1, k + 1):
m += abs(arr[0] - arr[i])
result.append(m)
else:
while left >= 0 and right < n:
lside = abs(arr[left] - arr[i])
rside = abs(arr[right] - arr[i])
if lside < rside:
m += lside
left -= 1
k -= 1
if k == 0:
break
else:
m += rside
right += 1
k -= 1
if (k == 0):
break
while left >= 0 and k > 0:
m += abs(arr[left] - arr[i])
left -= 1
k -= 1
while right < n and k > 0:
m += abs(arr[right] - arr[i])
k -= 1
right += 1
result.append(m)
return result
Nearest house โ
@classmethod
def compute(cls, input1, input2):
n = input1
arr = input2
result = []
for i in range(n):
m = 0
k = n - 2
left = i - 1
right = i + 1
if i == 0:
for i in range(1, k + 1):
m += abs(arr[0] - arr[i])
result.append(m)
else:
while left >= 0 and right < n:
lside = abs(arr[left] - arr[i])
rside = abs(arr[right] - arr[i])
if lside < rside:
m += lside
left -= 1
k -= 1
if k == 0:
break
else:
m += rside
right += 1
k -= 1
if (k == 0):
break
while left >= 0 and k > 0:
m += abs(arr[left] - arr[i])
left -= 1
k -= 1
while right < n and k > 0:
m += abs(arr[right] - arr[i])
k -= 1
right += 1
result.append(m)
return result
Nearest house โ
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I can't believe how far we've come together on this journey. A big thank you to each and every one of you for supporting me, leaving kind comments, and pushing me to keep creatingโคโ๐ป