Intro to data visualization libraries for ML in Python - Matplotlib, Seaborn, Plotly

Mastering Data Visualization for Machine Learning - Matplotlib, Seaborn & Plotly

Welcome to this comprehensive lecture on Data Visualization for Machine Learning, where we explore how to understand, interpret, and communicate data insights using three of Python’s most powerful libraries: Matplotlib, Seaborn, and Plotly.

In this hands-on session, we use the Titanic dataset to walk through the complete visualization workflow that every machine learning practitioner should master.

If you have ever struggled to convince your clients, professors, or stakeholders about your model’s results, this lecture is for you. Because great models alone do not inspire trust - great visualizations do.

What You Will Learn:

-Why visualization is essential in ML workflows
-Foundations of matplotlib: line plots, bar charts, histograms, and scatter plots
-Statistical plots with seaborn: countplot, boxplot, violin plot, correlation heatmap, and pairplot
-Interactive visualizations with plotly: 2D and 3D scatter plots, hoverable histograms and more

-How to perform Exploratory Data Analysis (EDA) using the Titanic dataset
-How to engineer features and clean data visually
-How to derive insights and communicate your findings through plots

📊 Datasets Used:

Titanic dataset (available through Seaborn)
Iris dataset (for multi-class plots)

🎯 Target Audience:

Machine learning beginners and intermediates
Data science students
Professionals transitioning to ML roles
Anyone who wants to see their data better

📌 Why This Lecture Matters: Machine learning is not just about accuracy scores. It is about trust, clarity, and decisions. Visualizations help you spot patterns, build intuition, and explain complex models in simple terms.

Whether you are preparing for a Kaggle competition, an academic thesis, or a stakeholder presentation, this lecture will equip you with the tools to build not just better models, but better narratives.

📢 Subscribe for more lectures on Machine Learning, Data Science, and AI.
Let us know in the comments if you want deep dives on specific visualization techniques or a follow-up tutorial using real-world business data.

[DhanasekaranN-rg]

As a developer, i had aversion to these plot/graph, you made it look easy. Frankly you explained very well, was able to learn and feel comfortable with ease. Can understand effort you take to prepare and render. Thank You!

[rainadkhan]

Amazing as always, Dr. Sreedath! This lecture was extremely helpful. Take love and God bless! ❤

[austineadah]

You are doing a great job sir and your series have inspired me to learn and do some research using ML. However I have some challenges:how can I draw Neural network architecture effectively and efficiently especially for paper publications?
Thank you