Foundations for Machine Learning

Currently Playing: Pandas in Python for ML and Data Science: A comprehensive introduction for beginners [Lecture 33]

Pandas for Beginners | Learn Data Analysis in Python 📊 Learn Pandas – The Most Powerful Python Library for Data Analysis! Welcome to Lecture 2 of our Python for Data Science and Machine Learning series! In this beginner-friendly video, you'll learn how to use Pandas, one of the most essential libraries in the data science toolkit. Whether you're an engineering student, a working professional, or someone exploring data science for the first time, this video will give you hands-on experience in loading, cleaning, analyzing, and summarizing real-world datasets — all using Python and Pandas. 🧠 What You’ll Learn in This Video: ✅ What is Pandas and why it’s important ✅ Understanding Series and DataFrame in Pandas ✅ How to read CSV files and explore datasets ✅ Indexing, slicing, and filtering rows and columns ✅ Handling missing data (NaN values) ✅ Aggregating and grouping data with .groupby() ✅ Sorting, merging, and joining DataFrames ✅ Hands-on Mini Project using the Titanic dataset! 📁 Resources Mentioned in the Video 🔗 Titanic Dataset: https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv 🔗 Iris Dataset: https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv 🔗 Google Colab Starter Notebook: (Add your link here) 📝 Mini Project Covered We explore the Titanic dataset to uncover: Survival patterns by gender and class Handling missing values in Age Grouping data for deep insights BONUS: Creating new features like AgeGroup for richer analysis 🧑‍🏫 Who is this video for? This lecture is perfect for: Beginners in Python or programming Students in engineering, mathematics, or science Working professionals looking to upskill in data analysis or ML 📌 Watch the Full Series 📍 Lecture 1: NumPy for Data Science → [Add link here] 📍 Lecture 3: Data Visualization with Matplotlib & Seaborn → [Coming Soon] 📍 Lecture 4: Introduction to Machine Learning with Scikit-learn → [Coming Soon] 🙌 Subscribe for More If you found this video helpful, please: 👍 Like 💬 Comment your questions 🔔 Subscribe for upcoming lectures on Python, ML, and real-world projects!