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NumPy Basics

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 మీరు AI Engineer/Data Scientist వైపు వెళ్లాలనుకుంటే NumPyలో ఈ టాపిక్స్‌ను క్రమంగా నేర్చుకోవడం చాలా ఉపయోగకరం.

1. NumPy Basics

  • NumPy అంటే ఏమిటి?
  • Installation
  • import numpy as np
  • Arrays సృష్టించడం 
np.array()
np.zeros()
np.ones()
np.arange()
np.linspace()

2. Array Attributes

Array గురించి సమాచారం తెలుసుకోవడం

arr.shape
arr.ndim
arr.size
arr.dtype

ఉదాహరణ:

arr = np.array([[1,2,3],[4,5,6]])

print(arr.shape)
print(arr.ndim)

3. Indexing & Slicing

ఇది చాలా ముఖ్యమైన టాపిక్.

arr[0]
arr[1:4]
arr[:,1]
arr[1,:]

4. Reshaping Arrays

arr.reshape()
arr.flatten()
arr.ravel()

ఉదాహరణ:

arr = np.arange(12)

arr.reshape(3,4)

5. Array Operations

+
-
*
/
**
%

ఉదాహరణ:

a + b
a * b

6. Broadcasting ⭐

NumPyలో అత్యంత ముఖ్యమైన కాన్సెప్ట్.

arr = np.array([1,2,3])

arr + 10

Output:

[11 12 13]

7. Mathematical Functions

np.sum()
np.mean()
np.max()
np.min()
np.std()
np.median()

8. Aggregation Functions

np.sum(axis=0)
np.sum(axis=1)
np.mean(axis=0)

Matrix calculations లో చాలా ఉపయోగపడుతుంది.

9. Boolean Masking & Filtering ⭐

arr[arr > 50]

ఉదాహరణ:

marks = np.array([40,60,70,30])

print(marks[marks >= 50])

Output:

[60 70]

10. Sorting

np.sort()
np.argsort()

11. Stacking & Splitting Arrays

np.concatenate()
np.vstack()
np.hstack()
np.split()

12. Random Module ⭐

Machine Learningలో చాలా ఉపయోగిస్తారు.

np.random.rand()
np.random.randint()
np.random.seed()

ఉదాహరణ:

np.random.randint(1,100,10)

13. Matrix Operations ⭐⭐⭐

np.dot()
np.matmul()
np.transpose()

ఉదాహరణ:

A = np.array([[1,2],[3,4]])
B = np.array([[5,6],[7,8]])

np.dot(A,B)

14. Linear Algebra Basics

np.linalg.inv()
np.linalg.det()
np.linalg.eig()

Machine Learning & Deep Learningలో ఉపయోగపడుతుంది.

15. Statistical Functions

np.mean()
np.var()
np.std()
np.percentile()

16. File Handling

np.save()
np.load()
np.savetxt()
np.loadtxt()

17. Performance Optimization

ఎందుకు NumPy Lists కంటే వేగంగా ఉందో తెలుసుకోండి.

import time

Time comparison చేయండి.

18. Real-world Data Analysis Practice

ఈ mini projects చేయండి:

  1. Student Marks Analysis
  2. Employee Salary Analysis
  3. IPL Dataset Analysis
  4. Sales Report Analysis
  5. Stock Price Analysis

AI/ML కోసం తప్పనిసరిగా నేర్చుకోవాల్సిన Top 8

  1. Array Creation
  2. Indexing & Slicing
  3. Reshaping
  4. Broadcasting
  5. Mathematical Functions
  6. Boolean Masking
  7. Matrix Multiplication
  8. Random Numbers

ఈ 8 టాపిక్స్ బాగా వచ్చితే, తర్వాత Pandas, Scikit-learn, మరియు TensorFlow నేర్చుకోవడం చాలా సులభమవుతుంది.

మీ 10 సంవత్సరాల డెవలపర్ బ్యాక్‌గ్రౌండ్‌ను బట్టి, రోజుకు 1–2 గంటలు ఇస్తే NumPyని 5–7 రోజుల్లో మంచి స్థాయిలో నేర్చుకోవచ్చు.

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