🔰 SECTION 1: Basics (Beginner Friendly)
- What is NumPy in Python? (Overview & Benefits)
- Installing NumPy (pip, conda, setup)
- NumPy Arrays vs Python Lists
- Creating NumPy Arrays (arange, linspace, zeros, ones)
- Array Attributes (shape, size, dtype, ndim)
🔢 SECTION 2: Array Operations
- Indexing & Slicing in NumPy
- Boolean Indexing & Filtering
- Mathematical Operations on Arrays
- Broadcasting Explained (Important for SEO 🔥)
- Copy vs View in NumPy
📊 SECTION 3: Functions & Computations
- Statistical Functions (mean, median, std)
- Aggregation Functions (sum, min, max)
- Sorting & Searching Arrays
- Linear Algebra Basics (dot, inverse)
- Random Module in NumPy
📐 SECTION 4: Advanced Concepts
- Reshaping Arrays (reshape, flatten, ravel)
- Stacking & Splitting Arrays
- Working with Multi-Dimensional Arrays
- Iterating Over Arrays
- Memory Optimization & Performance
🔗 SECTION 5: Real-World Use Cases
- NumPy for Data Science
- NumPy with Pandas
- NumPy with Matplotlib
- Image Processing using NumPy
- Machine Learning Basics with NumPy
🚀 SECTION 6: Expert Level
- Vectorization vs Loops (Performance Boost)
- Custom Functions with NumPy
- Debugging NumPy Code
- Common Errors & Fixes
- NumPy Interview Questions
