NumPy – Use in AI

Aim

This course builds strong NumPy fundamentals for AI and data workflows. Participants learn how to create, manipulate, and compute with arrays efficiently—covering vectorization, broadcasting, indexing, and basic linear algebra used in machine learning pipelines.

Who This Course Is For

• Beginners starting AI/ML and data science
• Students (UG/PG) learning Python for technical computing
• Researchers and professionals who work with numerical data
• Anyone planning to use Pandas, scikit-learn, or deep learning libraries

Prerequisites

• Basic Python syntax (variables, loops, functions)
• Basic math is enough (matrices are introduced from scratch)
• No prior ML experience required

What You’ll Learn

• Arrays: creating arrays, shapes, dtype, and memory basics
• Indexing and slicing: 1D/2D/3D operations
• Vectorization: replacing loops with fast array operations
• Broadcasting: rules and practical use in ML features
• Universal functions (ufuncs): math, comparisons, masking
• Reshaping: reshape, transpose, stack, concatenate
• Statistics: mean/median/std, normalization, and scaling concepts
• Linear algebra: dot product, matrix multiplication, solving basics
• Random module: sampling, shuffling, train-test split preparation
• Working with files: loadtxt, genfromtxt, save, npz

Program Structure

Module 1: NumPy Basics for AI

• Why NumPy is the base layer of ML and data tools
• Arrays vs Python lists: performance and use-cases
• Shapes, types, and simple computations

Module 2: Indexing, Slicing, and Masking

• Basic slicing and advanced indexing
• Boolean masks for filtering datasets
• Common pitfalls and best practices

Module 3: Vectorization and Broadcasting

• Vectorized operations for feature engineering
• Broadcasting rules with real examples
• Speeding up computations for ML pipelines

Module 4: Reshaping and Data Preparation

• Reshape and transpose for ML inputs
• Stacking, concatenation, and splitting arrays
• Handling missing values (basics)

Module 5: Stats and Normalization for ML

• Descriptive statistics for datasets
• Standardization and min-max scaling concepts
• Outlier handling using masks (intro)

Module 6: Linear Algebra Essentials

• Dot product and matrix multiplication
• Norms and similarity basics (overview)
• How these operations appear in ML models

Module 7: Randomness and ML Sampling

• Random sampling and reproducibility
• Shuffling and splitting datasets
• Generating synthetic data for testing

Module 8: Mini-Tasks for AI Readiness

• Prepare a dataset: clean, transform, normalize, and split
• Build simple feature matrices using vectorization
• Compute similarity and basic scoring using linear algebra

Tools & Workflow Covered

• Python + Jupyter/Colab
• NumPy core functions used in ML preprocessing
• Basic best practices for clean and efficient array code

Outcomes

• Write efficient NumPy code using vectorization and broadcasting
• Prepare numerical datasets for ML workflows
• Use NumPy linear algebra operations commonly used in AI
• Build confidence for moving to Pandas, scikit-learn, and deep learning

Certificate Criteria (Optional)

• Complete practice checkpoints
• Submit one mini-task notebook (data prep + transformations)