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Section 4 - Complete Numpy - Basic to Advanced

Welcome to the series - Complete Numpy - Basic to Advanced. Notes Link : Complete Numpy - Basic to Advanced

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Lecture 4.1) Introduction to NumPy - Why is NumPy Fast? - NumPy vs Pandas vs Lists - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Introduction to NumPy
  2. Why is NumPy so fast and popular?
  3. NumPy Vs Pandas explained with examples
  4. NumPy Vs Lists explained with examples
  5. Why is NumPy important in machine learning ?

#NumPy #PythonForDataScience #NumPyVsPandas #NumPyVsLists #DSWithPython #MachineLearningHindi #PythonLibraries #NumPyHindi #DSAHindi #PythonBeginners #decodeAiML

Lecture 4.2) NumPy Data Types Explained - Homogeneous vs Heterogeneous Arrays - NumPy Full Tutorial - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Creating a Numpy Array in Python
  2. 1D, 2D, and 3D arrays using Python Lists
  3. Data Types in Numpy Arrays
  4. Does Numpy support both Homogeneous and Heterogeneous Data?
  5. Representing Heterogeneous Data using Record and Structured Arrays
  6. Representing Heterogeneous Data using Object Arrays

#NumpyTutorial #NumpyDataTypes #PythonHindi #DataScienceHindi #MachineLearningHindi #PythonForBeginners #decodeAiML

Lecture 4.3) Creating NumPy Arrays - Basics - Reading & Writing NumPy Arrays (Text & Binary Files) - Hindi

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Video Description:

📘 Topics Covered:

  1. Creating NumPy arrays using numpy.array()
  2. Creating NumPy arrays using numpy.asarray()
  3. Understanding upcasting in NumPy arrays
  4. Reading and writing NumPy data to text files
  5. Reading and writing NumPy data to binary files
  6. Using .npy and .npz formats for binary file operations
  7. Advantages of binary files & limitations of text files

#NumPyTutorial #PythonForDataScience #NumPyArrays #PythonTutorial #DataScienceWithPython #NumPyHindi #DecodeAiML

Lecture 4.4) NumPy Math Basics - Diagonal, Identity, Triangular Matrix, Random & Log/Linear Space - Hindi

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Video Description:

📘 Topics Covered:

  1. Linear Space and Logarithmic Space with Examples
  2. Vector, Matrix, Tensor, numpy Array Explained with Examples
  3. Square and Rectangular Matrix Explained
  4. Diagonal of a Matrix (Main & k-Offset Diagonals)
  5. Identity, Upper & Lower Triangular Matrix Explained
  6. Random Number Generation using numpy.random.rand() and numpy.random.randn()
  7. Uniform and Normal Distribution Explained with Examples

#NumPy #PythonForDataScience #NumPyTutorial #PythonTutorialHindi #MachineLearning #DataScience #NumPyMath #MatrixAlgebra #PythonBeginners #DecodeAiML

Lecture 4.5) NumPy Arrays with Built‑Ins - arange, linspace, logspace, diag, eye, tri, rand, randn - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Linear & Logarithmic Space using np.linspace() and np.logspace()
  2. range() vs np.arange() in Python and NumPy
  3. Square vs Rectangular Matrices Explained
  4. Main & k‑Offset Diagonal using np.diag()
  5. Identity & Triangular Matrices using np.eye(), np.tri(), np.tril(), np.triu()
  6. Random Number Generation using np.random.rand() & np.random.randn()
  7. Uniform vs Normal Distribution Explained with Examples

#NumPy #PythonForDataScience #NumPyTutorial #PythonHindi #MachineLearning #DataScience #NumPyArrays #PythonTutorial #DecodeAiML

Lecture 4.6) Dimension, Shape, Axis, Vectorization & Broadcasting in NumPy - Explained with Examples - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Understanding Dimensions in NumPy Arrays
  2. Shape & Axis in NumPy Explained
  3. Broadcasting in NumPy with Examples and its Rules
  4. Vectorization in NumPy with Examples and Key Advantages
  5. Performance Comparison: Vectorization vs Traditional Loops

#NumPy #PythonDSA #MachineLearning #DataScience #Vectorization #Broadcasting #PythonTutorial #PythonHindi #ArrayOperations #PythonProgramming #DecodeAiML

Lecture 4.7) Hands-on Indexing, Slicing & Subsetting in NumPy - Explained with Examples - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Indexing with examples for NumPy arrays
  2. Slicing with examples for NumPy arrays
  3. Subsetting with examples for NumPy arrays
  4. Views vs Copy in NumPy explained with examples
  5. sort() and max() in NumPy Explained

#NumPy #PythonDSA #DataScience #NumPyIndexing #NumPySlicing #NumPyTutorial #PythonProgramming #DSAInPython #PythonHindi #CodingForBeginners #DecodeAiML

**Lecture 4.8) Hands-On Maths on NumPy Arrays Built-In Maths Functions Broadcasting explained Hindi**

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Mathematical operators on Numpy Array
  2. Broadcasting explained with examples
  3. Built-in function in Numpy explained with examples
  4. Add, Subtract, Multiply, Divide, Exp, sqrt and Log operations

#NumPy #PythonDSA #MathWithNumPy #Broadcasting #PythonProgramming #NumPyTutorial #NumPyMath #PythonHindi #DSAInPython #CodingForBeginners #DecodeAiML

Lecture 4.9) NumPy Statistics & Linear Algebra - Axis Explained - Built-in Functions - Hands-On - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Statistics methods: Mean, Median, Mode, Variance, Standard Deviation
  2. Correlation and Covariance explained
  3. Scatter plot using Matplotlib for correlation
  4. Axis explained with NumPy examples
  5. Percentile and Quantile explained with built-ins
  6. Linear Algebra methods: Sum, Product, Diagonals, Trace, Transpose
  7. Dot, Inner, and Outer product explained with examples

#NumPy #PythonDSA #NumPyStatistics #NumPyLinearAlgebra #AxisInNumPy #PythonProgramming #NumPyTutorial #PythonHindi #DSAInPython #CodingForBeginners #NumPyMath #DataScienceWithPython #decodeAiML

Lecture 4.10) NumPy Built-in Functions - ravel, moveaxis, squeeze, concatenate, stack, split, repeat - Hindi

Video Link: Watch on YouTube

Video Description:

📘 Topics Covered:

  1. Introduction to built-in NumPy functions
  2. ravel() usage explained
  3. moveaxis() usage explained
  4. squeeze() usage explained
  5. concatenate() usage explained
  6. stack() usage explained
  7. split() usage explained
  8. tile() usage explained
  9. repeat() usage explained
  10. append() usage explained
  11. unique() usage explained

#NumPy #NumPyFunctions #PythonDSA #PythonProgramming #NumPyTutorial #PythonHindi #DataScienceWithPython #DSAInPython #CodingForBeginners #NumPyArrayManipulation #PythonNumPy #DecodeAiML

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