Chapter 1: Introduction to NumPy
- Lesson 1: What is NumPy?
- Lesson 2: History and Importance of NumPy
- Lesson 3: Installing NumPy (Windows, macOS, Linux)
- Lesson 4: Importing and Using NumPy
- Lesson 5: Understanding NumPy Arrays vs Python Lists
Chapter 2: NumPy Arrays Basics
- Lesson 1: Creating NumPy Arrays (1D, 2D, 3D)
- Lesson 2: Data Types in NumPy Arrays
- Lesson 3: Indexing and Slicing Arrays
- Lesson 4: Reshaping and Resizing Arrays
- Lesson 5: Copying vs Viewing Arrays
Chapter 3: Array Operations and Broadcasting
- Lesson 1: Arithmetic Operations on Arrays
- Lesson 2: Element-wise Operations
- Lesson 3: Universal Functions (ufuncs)
- Lesson 4: Broadcasting in NumPy
- Lesson 5: Aggregation Functions (sum, min, max, etc.)
Chapter 4: NumPy Array Manipulation
- Lesson 1: Stacking and Splitting Arrays
- Lesson 2: Adding and Removing Elements
- Lesson 3: Sorting Arrays
- Lesson 4: Searching Elements in Arrays
- Lesson 5: Filtering Arrays with Conditions
Chapter 5: Working with NumPy Data Types
- Lesson 1: Understanding NumPy Data Types
- Lesson 2: Converting Data Types (astype)
- Lesson 3: Structured Arrays in NumPy
- Lesson 4: Memory Layout of NumPy Arrays
- Lesson 5: Byte Swapping and Endianness
Chapter 6: Random Number Generation
- Lesson 1: Introduction to NumPy’s Random Module
- Lesson 2: Generating Random Integers and Floats
- Lesson 3: Creating Random Distributions (Normal, Uniform, etc.)
- Lesson 4: Seeding Random Generators
- Lesson 5: Shuffling and Sampling Arrays
Chapter 7: NumPy Mathematics Functions
- Lesson 1: Basic Mathematical Operations
- Lesson 2: Trigonometric Functions in NumPy
- Lesson 3: Logarithmic and Exponential Functions
- Lesson 4: Rounding and Floor/Ceil Functions
- Lesson 5: Handling NaN and Infinite Values
Chapter 8: NumPy Linear Algebra Basics
- Lesson 1: Introduction to NumPy Linear Algebra
- Lesson 2: Matrix Multiplication (dot, matmul)
- Lesson 3: Determinants and Inverse of Matrices
- Lesson 4: Eigenvalues and Eigenvectors
- Lesson 5: Solving Linear Equations with NumPy
Chapter 9: File Handling in NumPy
- Lesson 1: Reading and Writing Text Files
- Lesson 2: Reading and Writing CSV Files
- Lesson 3: Handling Binary Files with NumPy
- Lesson 4: Memory Mapping Large Files
- Lesson 5: Using NumPy's Save and Load Functions
Chapter 10: Boolean and Advanced Indexing
- Lesson 1: Boolean Array Operations
- Lesson 2: Masking Arrays
- Lesson 3: Advanced Indexing Techniques
- Lesson 4: Fancy Indexing
- Lesson 5: Combining Boolean and Fancy Indexing
Chapter 11: Working with Dates and Time in NumPy
- Lesson 1: NumPy DateTime Basics
- Lesson 2: Creating Date Ranges
- Lesson 3: Date Arithmetic in NumPy
- Lesson 4: Converting Strings to DateTime
- Lesson 5: Working with Timestamps
Chapter 12: Performance Optimization with NumPy
- Lesson 1: Understanding NumPy Performance
- Lesson 2: Vectorization and Avoiding Loops
- Lesson 3: Memory Efficiency in NumPy
- Lesson 4: Profiling NumPy Code
- Lesson 5: When to Use NumPy vs Pandas
Chapter 1: Advanced NumPy Array Operations
- Lesson 1: Array Views vs Copies
- Lesson 2: Strided Views in NumPy
- Lesson 3: Fancy Indexing Internals
- Lesson 4: Using np.lib for Internal Functions
- Lesson 5: Understanding NumPy’s Buffer Protocol
Chapter 2: Broadcasting and Advanced Arithmetic
- Lesson 1: Deep Dive into Broadcasting
- Lesson 2: Custom Broadcasting Rules
- Lesson 3: Applying Broadcasting for Efficiency
- Lesson 4: Memory Overhead in Broadcasting
- Lesson 5: Pitfalls and Debugging Broadcasting Errors
Chapter 3: NumPy and Fourier Transforms
- Lesson 1: Introduction to Fourier Transforms
- Lesson 2: Using np.fft for Signal Processing
- Lesson 3: Fast Fourier Transform (FFT) Applications
- Lesson 4: Inverse FFT and Real FFT
- Lesson 5: Working with Power Spectrums
Chapter 4: Advanced Linear Algebra with NumPy
- Lesson 1: Singular Value Decomposition (SVD)
- Lesson 2: QR Decomposition
- Lesson 3: Cholesky Decomposition
- Lesson 4: Pseudo-Inverse and Moore-Penrose Inverse
- Lesson 5: Advanced Eigenvalue Problems
Chapter 5: Sparse Matrices with NumPy
- Lesson 1: Introduction to Sparse Matrices
- Lesson 2: Storing Sparse Data Efficiently
- Lesson 3: Converting Between Dense and Sparse Matrices
- Lesson 4: Operations on Sparse Matrices
- Lesson 5: Applications in Data Science
Chapter 6: Parallel Computing with NumPy
- Lesson 1: Introduction to Parallelization
- Lesson 2: Using NumPy with Multiprocessing
- Lesson 3: Vectorized Parallel Processing
- Lesson 4: NumPy with OpenMP
- Lesson 5: Using Numba for Faster Computation
Chapter 7: Integration with Other Libraries
- Lesson 1: NumPy with Pandas
- Lesson 2: NumPy with SciPy
- Lesson 3: NumPy with Matplotlib
- Lesson 4: NumPy with TensorFlow and PyTorch
- Lesson 5: NumPy with OpenCV
Chapter 8: Working with Large Datasets
- Lesson 1: Handling Large NumPy Arrays
- Lesson 2: Using Memory-Mapped Files for Large Data
- Lesson 3: Optimizing NumPy Performance for Big Data
- Lesson 4: Distributed Computing with Dask and NumPy
- Lesson 5: GPU Acceleration with CuPy
Chapter 9: Debugging and Error Handling in NumPy
- Lesson 1: Understanding NumPy Errors
- Lesson 2: Debugging Array Shape Errors
- Lesson 3: Handling Overflow and Underflow Errors
- Lesson 4: Performance Bottlenecks and Fixes
- Lesson 5: Writing Robust NumPy Code
Chapter 10: NumPy in Machine Learning
- Lesson 1: Implementing Linear Regression with NumPy
- Lesson 2: Logistic Regression Using NumPy
- Lesson 3: Neural Networks from Scratch with NumPy
- Lesson 4: Image Processing with NumPy
- Lesson 5: NumPy for Reinforcement Learning
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