Day 2 :-Python for ML:

NumPy & Pandas Basics

Hello everyone! 👋
Welcome to Day 2 of my #30DaysOfML journey.

Today was all about getting hands-on with the core Python libraries used in Machine Learning: NumPy and Pandas.

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These two are the backbone of any data science libraries helping us clean, explore, and manipulate data efficiently.

Why NumPy & Pandas? What I Did Today:

🔹 NumPy:

• Created arrays
• Used basic operations like .shape, .reshape(), .mean(), .sum(), and slicing
• Explored broadcasting and array math
• NumPy (Numerical Python) is a powerful library for numerical operations. Think of it as Python’s faster calculator 🧮.
• Pandas is built on top of NumPy and helps us handle structured data using DataFrames — like Excel in Python, but on steroids!

Pandas:

• Loaded datasets using pd.read_csv()
• Explored columns, rows, and info using .head(), .info(), .describe()
• Filtered data, handled missing values, and used .groupby()

📌 Tip: Use .isnull().sum() to quickly check missing data in a dataset!