Introduction:

In the ever-evolving world of AI, knowing when to use Machine Learning or Deep Learning can make all the difference. This project is my deep dive into understanding both — not just by the book, but through real-world context and thoughtful comparison.

Description:

I explored the fundamental differences between Machine Learning (ML) and Deep Learning (DL) — from how they learn to how much data they need to what kinds of problems they solve best. This artifact breaks down each approach’s strengths, trade-offs, and ideal use cases with visuals and insights that make complex concepts easy to grasp.

Objective:

• Clarify how ML and DL differ in approach, complexity, and impact

• Help others (and myself!) make smarter choices when building AI solutions

• Build a practical, go-to framework for selecting the right method in future projects

Process:

• Exploration: I started with a question: What really separates ML from DL, and when should I use each?

• Research: Dived into trusted AI resources (like IBM, AWS etc) for current definitions, trends, and examples

• Comparison Framework: Created side-by-side breakdowns of key factors: data size, computation, interpretability, and real-world fit.

• Real-Life Scenarios: Mapped each method to relatable use cases — like fraud detection, image recognition, and language processing.

• Presentation: Designed the content to be clear, engaging, and easy to reference — for peers, mentors, or my future self.

Tools and Technologies Used:

• Visual tools (Google Docs) for comparison charts

• Trusted AI learning platforms (IBM, AWS articles)

• Real-world case study examples for context

Value Proposition:

This project shows I don’t just know the theory — I can explain it, apply it, and help others understand it too. It reflects my analytical thinking, clarity of communication, and ability to turn complex tech into approachable insights.

Unique Value:

Most comparisons stop at definitions. This one goes further — into mindset, usage, and decision-making. I bring a human lens to technical AI topics, making this more than just a chart… it’s a tool for smarter AI thinking.

Relevance:

Whether designing a chatbot or optimizing a model for limited data, understanding ML vs. DL is foundational. This artifact proves I’m ready to make those decisions with confidence, logic, and some storytelling magic.

References:

IBM: AI vs. ML vs. DL vs. Neural Networks

AWS: Difference Between Machine Learning and Deep Learning

Generative AI tools, such as ChatGPT, Perplexity, etc., were used for background research, restructuring, and rephrasing.