NUMPY

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numpy
Why NumPy is Important
in Data Science and AI

NumPy is the backbone of numerical computing in Python and forms the basis for most data science libraries.

It provides efficient, fast operations on large multi-dimensional arrays and matrices. NumPy's vectorization avoids slow Python loops, leading to significant performance improvements. Libraries like Pandas, SciPy, Scikit-learn, TensorFlow, and PyTorch depend on NumPy arrays.

 

It includes powerful mathematical, statistical, and linear algebra operations critical for AI algorithms. NumPy facilitates data preprocessing, feature scaling, and numerical simulations. It supports broadcasting, masking, and advanced indexing to manipulate data effectively.

With NumPy, data scientists can prototype and test algorithms efficiently. Its integration with C, C++, and Fortran supports high-performance computing.

Understanding NumPy equips learners with the skills to handle real-world datasets and model inputs.

01

Module 1: Introduction to NumPy

  1. What is NumPy?
  2. Installing NumPy
  3. Importing NumPy with import numpy as np
  4. Differences between Python Lists and NumPy Arrays
  5. Use cases of NumPy in DS/AI
02

Module 2: NumPy Arrays (ndarray)

  1. Creating 1D, 2D, and 3D arrays
  2. Checking shape, size, and dimensions
  3. Data types and dtype attribute
  4. Type casting and conversion
  5. Reshaping arrays
03

Module 3: Array Indexing and Slicing

  1. Basic indexing and slicing
  2. Negative indexing
  3. Slicing multi-dimensional arrays
  4. Fancy indexing
  5. Boolean indexing
  6. Using np.where(), np.argmax(), np.argmin()
04

Module 4: Array Operations

  1. Element-wise arithmetic operations
  2. Array comparison and logical operations
  3. Vectorized operations
  4. Aggregations: sum, mean, std, min, max, cumsum, prod
  5. Axis-wise operations
05

Module 5: Array Manipulation

  1. Reshape, Flatten, Ravel
  2. Transpose and Swapaxes
  3. Concatenation and Splitting (hstack, vstack, concatenate, split)
  4. Tiling and Repeating arrays
  5. Broadcasting rules 
06

Module 6: Linear Algebra with NumPy

  1. Matrix creation with np.matrix or np.array
  2. Matrix multiplication (dot, matmul)
  3. Identity and diagonal matrices
  4. Determinant, Inverse, Transpose
  5. Eigenvalues and Eigenvectors
  6. Solving systems of linear equations (np.linalg.solve)
07

Module 7: Random Module in NumPy

  1. Random number generation: rand, randn, randint
  2. Setting seed with np.random.seed()
  3. Shuffling and Permutations
  4. Normal, Uniform, and Binomial distributions
  5. Generating random samples
08

Module 8: Useful NumPy Functions

  1. np.linspace, np.arange, np.meshgrid
  2. np.unique, np.sort, np.argsort
  3. np.any, np.all
  4. np.clip, np.round, np.floor, np.ceil
  5. np.nan, np.isnan, np.nan_to_num
09

Module 9: Advanced NumPy Concepts

  1. Memory layout: C_CONTIGUOUS, F_CONTIGUOUS
  2. Broadcasting in detail
  3. Structured arrays and record arrays
  4. Stride tricks
  5. Vectorization and performance optimization
10

Module 10: Integration with Other Libraries

  1. NumPy and Pandas: Converting between Series/DataFrames and arrays
  2. NumPy and Matplotlib for plotting
  3. NumPy in Scikit-learn and TensorFlow
  4. Using NumPy arrays with OpenCV (image data)

Click here to watch the recorded NUMPY playlist.

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