DEV Community: Urooj Fatima

Will AI take our jobs?

Urooj Fatima — Thu, 09 Apr 2026 13:13:40 +0000

We all hear this everywhere:

“AI will take our jobs.”

But is that really true?

Let’s talk about reality.

AI is not your enemy.
It’s a tool—the most powerful tool of this era.

And like every major technology shift in history…
it’s changing the way we work, not eliminating work itself.

Today, AI can:
• Automate repetitive tasks
• Help write code faster
• Analyze large amounts of data in seconds
• Assist in decision-making

But here’s what it can’t replace:

Human creativity
Critical thinking
Emotional intelligence
Real-world problem solving

That’s where you come in.

The truth is:

AI won’t replace you.
But someone using AI might.

The real risk is not AI…
It’s staying the same while the world is evolving.

If we don’t learn it, we get left behind.
If we do learn it, we move ahead

Tools don’t take opportunities away—
they create new ones for those who know how to use them.

AI is not here to be your master.
It’s here to be your advantage.

The question is:

Will you ignore it… or learn it?

What do you think—threat or opportunity?

Let’s talk

I stopped waiting to feel “ready”… and everything changed ✨

Urooj Fatima — Mon, 30 Mar 2026 14:46:56 +0000

I stopped waiting to feel “ready”… and everything changed ✨

Stepping into the tech world felt scary 😨

I used to wonder:
What is coding actually?
Can I really do this?
Everyone is already ahead of me… am I too late? ⏳

Coming from a non-technical background, I didn’t even understand something as simple as
print("Hello World") 💻

These thoughts held me back for a long time.

But one day, I made a decision:
It’s okay to start late… but it’s not okay to never start 🚀

So I stopped comparing myself to others.
It became me vs me 💯

I started exploring—C++, Python… slowly, step by step 🧠

And honestly? It wasn’t easy.

There were days I spent hours trying to fix a tiny bug 🐛
Days I felt stuck 😞
Even days I cried 💔

But I didn’t quit.

I kept going 💪

And that’s when everything started to change ✨

Today, I’ve built projects 🛠️
I’m still learning, still growing 📈
Turning code into something meaningful—study tools, AI experiments, and more 🤖

My GitHub might not be perfect or “green”…
But it shows one thing: I showed up. I tried. I didn’t give up. 🌱

And now I know—this curiosity I had?
It wasn’t random.
It was leading me somewhere 🌍

💬 I’d love to hear from you:

What’s something that once scared you… but you started anyway?
What keeps you going when things get hard?

Let’s connect, grow, and learn together 🤝

CodingJourney #WomenInTech #Beginners #LearnToCode #Consistency #TechLife #GrowthMindset

# Real-Time Object Detection & Tracking with YOLOv8 & Deep SORT 🚀

Urooj Fatima — Thu, 26 Mar 2026 18:50:50 +0000

Ever wondered if your computer could “see” and track objects in real-time? 👀

During my CodeAlpha internship, I built a system in Python that does exactly that. It detects objects in video files and live webcam streams, assigning each object a unique ID as it moves.

What I learned / challenges:

Objects sometimes got “lost” between frames — tweaking the tracking algorithm fixed it
Switching between video files and webcam input required careful testing
Managing Python dependencies and real-time performance was tricky

Tech Stack:

YOLOv8 (Ultralytics)
Deep SORT
OpenCV
Python

💡 Fun part: Watching the bounding boxes follow objects in real-time felt magical ✨

Check out my code and demo: [GitHub Repo]

Urooj25 / CodeAlpha_ObjectDetection

CodeAlpha_ObjectDetection

🔍 Real-Time Object Detection & Tracking
Internship Task 4 — CodeAlpha

📌 About This Project

This project implements real-time object detection and tracking using:

YOLOv8 (via ultralytics) for object detection
Deep SORT for tracking objects across frames
OpenCV for video input/output (webcam or video files)

It fulfills all internship requirements:

Real-time video input (webcam or video file)
Pre-trained model for object detection
Bounding boxes with tracking IDs
Python implementation with clear, real-time results

🚀 Features

Detects objects in every video frame
Tracks detected objects with unique IDs
Works with multiple video files or webcam input
Outputs video frames with bounding boxes and labels

📦 Installation

Clone the repository

git clone https://github.com/Urooj25/CodeAlpha_ObjectDetection.git cd CodeAlpha_ObjectDetection

2- Create a virtual environment python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate

3-Install dependencies pip install -r requirements.txt

Usage Run on Video Files Place your video(s) in the videos/ folder or detect real time…

View on GitHub

Question for you: Have you tried real-time object tracking? How did you approach it?

When My First ML Model Memorized Instead of Learning (And How I Fixed It)

Urooj Fatima — Sun, 22 Mar 2026 10:50:55 +0000

When I started working on my first machine learning projects, I thought I was doing everything right.

My model showed almost perfect accuracy during training, and I felt confident about the results.

But as soon as I tested it on new data… everything broke.

That’s when I learned one of the most important lessons in machine learning:
high accuracy doesn’t always mean your model is actually learning.

🧠 The Problem: Overfitting

The issue I faced was overfitting.

Because my dataset was relatively small, the model started memorizing the training data instead of learning general patterns. It captured noise, small variations, and specific details that didn’t apply to new data.

So while performance looked great during training, it completely failed in real-world scenarios.

🛠️ How I Fixed It (From My Projects)

While working on projects like E-commerce Churn Prediction and Diabetes Prediction, I focused on solving this problem step by step.

1. Handling Imbalanced Data with SMOTE

Instead of duplicating data points, I used SMOTE (Synthetic Minority Over-sampling Technique) to create balanced datasets.
This helped the model learn better patterns rather than biasing toward one class.

2. Using Cross-Validation

Rather than relying on a single train/test split, I applied K-Fold Cross Validation.
This gave me a more reliable estimate of how my model performs on unseen data.

3. Controlling Model Complexity

I used algorithms like Random Forest but made sure to tune parameters like tree depth.
Reducing complexity helped prevent the model from memorizing the data.

💡 Key Lesson

The biggest realization for me was:

A model that performs well on training data but fails on new data is not useful.

Generalization matters more than perfect accuracy.

This completely changed how I approach machine learning projects now. I focus more on real performance rather than just improving scores.

📌 Projects I Worked On

Here are some of the projects where I applied these concepts:

E-commerce Churn Prediction
Diabetes Prediction System
Python Practice Projects

(Links available on my GitHub profile)

🚀 Final Thoughts

I’m still learning and improving, but this experience helped me understand machine learning on a deeper level.

If you’re just starting out, don’t chase perfect accuracy — focus on building models that actually work on real data.

💬 Let’s Discuss

What was the biggest challenge you faced when starting machine learning?
Was it overfitting, data preprocessing, or understanding the concepts?

I’d love to hear your experience.

From Confusion to Code: My Journey into Tech as a Beginner 🚀

Urooj Fatima — Thu, 19 Mar 2026 19:11:52 +0000

This is a submission for the 2026 WeCoded Challenge: Echoes of Experience

Introduction

I belong to a small city in Pakistan, with a big dream — to get admission into one of the most prestigious universities in the country, NUST.

I worked really hard for it. I spent months studying, cramming everything I could, pushing myself through stress and pressure. After so much struggle, I finally got selected in Electrical Engineering. Although I had always dreamed of Software Engineering, I still felt proud to achieve this milestone.

But deep inside, something felt incomplete.

I had always heard about coding — how it can create things, solve problems, and even change lives. To me, it felt like magic. A world where you can build anything with logic and creativity. And I knew, I wanted to be part of that world.

After joining university, while studying circuits and electrical concepts, I realized that my true interest was somewhere else. That’s when I made a decision — I would enter the tech world on my own along with my circuits.

I started with the basics of C++, not knowing where this path would lead, but with one clear intention: to build something meaningful, something that could benefit others.

Struggles

I quickly realized that this journey was not going to be easy.

Coming from a non-technical background, everything felt new and confusing. There were times when I didn’t even understand basic concepts, and it made me question myself — am I really made for this?

Errors became a daily part of my life. Sometimes I would spend hours, even an entire day, just trying to fix a single small mistake. Sitting alone in my room, staring at the screen, trying everything I could… and still not getting the result.

It was frustrating, but strangely, it never made me quit.

There was no proper guidance, no clear roadmap. I explored things on my own — searching about what skills are in demand, trying to understand platforms like Fiverr, and figuring out how this whole tech world actually works.

At times, I compared myself with others who seemed to be moving faster, achieving more, and understanding things easily. It made me feel behind.

But then I realized something important — this is my journey, and it doesn’t have to look like anyone else’s.

Instead of giving up, I kept going. Every error I solved, every small concept I understood, gave me a little more confidence.

And slowly, I started enjoying the struggle.

Turning Point

At one point, I made a clear decision — I wanted to go deeper and truly understand the “magic” of coding.

I created my GitHub account and started turning my learning into real projects. Not just for practice, but to build something meaningful and to see my progress with my own eyes.

I also began sharing my journey on LinkedIn — not to show off, but to inspire others like me who feel lost at the beginning. I wanted to prove that with dedication and consistency, anything is possible.

I started with small projects like a calculator, but as my interest grew, I moved towards more advanced ideas. Since I wanted to explore AI, I built projects like diabetes prediction, customer churn prediction, student result analysis, translation tools, and chatbots.

Recently, I have been working on something very close to me — a personal study assistant that can help me learn better.

The more I explore this tech world, the more I feel connected to it. I want to go deeper, build bigger projects, and create something that truly makes a difference.

I know I am still at the beginning, and this journey is not easy. But I also know one thing for sure — it is possible.

Current Journey & Message

I am still building, still learning, and still exploring.

Right now, I am working on language translation tools and chatbots, while also trying to understand freelancing by creating and exploring my Fiverr account. My goal is simple — to turn my passion into independence.

This journey is continuous, and it is not easy.

There are bugs, challenges, and moments when I feel stuck. Sometimes I feel tired, sometimes disheartened. But I have learned that it’s okay to feel that way. What’s not okay is to stop trying because of it.

I know I still have a long way to go — a journey full of struggle, learning, hope, and dedication. But I believe that if I stay consistent, my passion will take me where I want to be.

More than anything, I want to inspire other girls like me.

Girls who feel lost. Girls who think they are behind. Girls who are afraid to start.

I want to show them that they can stand out, even in a world where it feels difficult.

Because when women rise in tech, they don’t just change their own lives — they make the world better.

wecoded #devchallenge #womenintech #beginners #programming

Stop chasing degrees. Start building projects.

Urooj Fatima — Wed, 18 Mar 2026 10:08:14 +0000

As a student exploring tech and trying to build real skills, I realized something important…
Degrees can open doors, but projects are what actually get you inside.

Let’s be honest.

You can spend 4 years collecting a degree…
Or you can spend 6–12 months building real skills that people can actually see.

And in today’s tech industry? Visibility beats theory.

Recruiters aren’t asking: “What did you study?”
They’re asking: “What have you built?”

If you’re an early-career developer or a student feeling stuck between traditional paths and real-world skills — this is your sign to shift direction.

🚀 The Reality Check: Degrees vs. Skills

A degree isn’t useless. But it’s no longer enough.

Thousands of graduates enter the market every year with similar qualifications. What makes someone stand out isn’t their GPA — it’s their proof of work.

Think about it:

A resume says you “know Python”
A project shows you built a real app using Python
A portfolio proves you can solve problems

That’s the difference.

The tech world rewards builders, not just learners.

💡 Section 1: Projects Are Your Real Resume

If your GitHub is empty, your resume is incomplete.

Your portfolio projects are the strongest signal you can send to employers. They demonstrate:

Problem-solving ability
Consistency
Practical knowledge
Creativity

And most importantly — they show initiative.

Real-world example:

Two candidates apply for the same role:

Candidate A

Degree in Computer Science
No projects

Candidate B

Self-taught
Built:
- A task manager app
- A weather dashboard
- A simple AI chatbot

Who gets noticed first?

Every time — it’s Candidate B.

🛠️ Section 2: How to Start Building (Even If You Feel “Not Ready”)

Here’s the truth: you’ll never feel ready.

Start anyway.

Step-by-step approach:

1. Start small
Don’t aim for the “next big startup.” Build simple things:

Calculator app
To-do list
Portfolio website

2. Follow → Then modify

Watch a tutorial
Rebuild it
Then add your own feature

3. Solve real problems
Look around you:

A system to manage your assignments
A tool to track study hours
A website for a local business

4. Build consistently

2–3 projects per month is powerful
Progress > perfection

📢 Section 3: Show Your Work (This Is Where Most People Fail)

Building is only half the game.

If no one sees your work, it doesn’t exist.

You need to show your work.

Where to share:

LinkedIn (weekly posts)
GitHub (clean repositories)
Personal portfolio website

What to share:

Project demos
Screenshots
Lessons learned
Challenges you solved

Example post idea:

“Built a To-Do App using React in 3 days.
Faced issues with state management but solved it using hooks.
Here’s what I learned…”

That’s powerful.

You’re not just coding — you’re building your personal brand.

🌐 Section 4: Build a Portfolio That Speaks for You

Your portfolio is your digital identity.

Make it simple, clean, and focused.

Must-have sections:

👤 About Me (short and clear)
💼 Projects (with live links + GitHub)
🛠️ Skills (tools you actually use)
📫 Contact

Portfolio tips:

Highlight 3–5 strong coding projects
Add screenshots or demos
Explain the problem → solution → result
Keep design minimal but professional

Pro tip:

Don’t just show what you built — explain why it matters.

🤝 Section 5: Share, Connect, Grow

Opportunities don’t just come from skills — they come from visibility.

Start building a presence.

Actionable steps:

Post on LinkedIn 2–3 times per week
Connect with developers and recruiters
Comment on others’ posts
Share your learning journey

You don’t need to be an expert.

You just need to be consistent.

Remember:

People don’t follow perfection.
They follow progress.

⚡ Final Thoughts: Shift Your Mindset

Stop waiting for permission.

Stop thinking:

“I need another course”
“I need another certificate”
“I need to be perfect first”

Start thinking:

“What can I build today?”
“What can I share this week?”
“How can I improve one step at a time?”

Because at the end of the day…

The tech industry doesn’t care how many PDFs you have.

It cares about:

What you’ve built
What you can solve
What you can show

🔥 Call to Action

If you’re serious about breaking into tech:

👉 Start your first project this week
👉 Share it publicly (even if it’s simple)
👉 Tag your journey with: #ShowYourWork #PortfolioProjects #SelfTaught

What’s one project you’re currently working on? Let’s connect and grow together 🚀

ShowYourWork #PortfolioProjects #SelfTaught #CodingProjects #TechCareers #Developers #LearnToCode #StudentDevelopers #BuildInPublic

Starting to Code as an Electrical Engineering Student — Why AI Matters

Urooj Fatima — Sat, 14 Mar 2026 16:22:32 +0000

When many students begin Electrical Engineering, they imagine circuits, complex equations, and long hours in laboratories. Coding is usually not the first thing that comes to mind. But in today’s technology-driven world, programming and Artificial Intelligence (AI) are becoming essential tools for electrical engineers.

When I first started my engineering journey, I assumed programming was mainly for computer science students. My focus was on understanding physics, mathematics, and circuit analysis. However, I soon realized that many engineering problems become much easier with coding.

For example, tasks like analyzing experimental data, plotting signals, or performing repetitive calculations can be done quickly with a small program. Instead of spending hours on manual calculations, code can solve these problems in seconds.

At the beginning, learning programming felt challenging. Understanding syntax, fixing errors, and balancing coding practice with university coursework was not always easy. Sometimes even a small mistake could stop an entire program from running. However, with consistent practice and small projects, coding gradually started to make sense.

For beginners in Electrical Engineering, starting with a language like Python is often the best approach. Python is widely used in engineering, data analysis, and artificial intelligence because of its simplicity and powerful libraries. Even learning basic concepts such as variables, loops, and functions can open the door to solving practical engineering problems.

One effective way to learn programming is by building small projects. For example, a beginner project could be creating a signal generator that produces and visualizes sine waves or square waves. Another interesting project could involve predicting electricity consumption using a simple machine learning model. These projects help students connect programming with real engineering applications.

Artificial Intelligence is also playing a growing role in Electrical Engineering. In modern power systems, AI helps predict electricity demand and manage energy distribution efficiently. In autonomous vehicles, AI analyzes sensor data to recognize objects and make driving decisions. Industries also use AI for predictive maintenance, where machines detect potential failures before they happen.

These examples show that electrical engineers today work at the intersection of hardware, software, and intelligent systems.

Balancing coding with demanding engineering coursework can be difficult, but small consistent efforts make a big difference. Practicing coding for even 30 minutes a day can gradually build strong programming skills. Applying coding to engineering assignments or laboratory data can also make learning more practical and engaging.

The future of Electrical Engineering is becoming increasingly interdisciplinary. Technologies such as smart grids, robotics, renewable energy systems, and intelligent communication networks rely heavily on both electronics and AI.

For students starting their journey today, learning coding is not just an extra skill—it is a powerful tool that can expand their ability to innovate and solve complex problems.

Every engineering journey begins with curiosity. Sometimes, the first step toward building intelligent systems and solving real-world challenges starts with something simple: writing the first line of code.