🧠 Beginner's Guide to Computer Vision: Learn and Crack Your First Tech Interview

Are you a recent graduate looking to break into the tech industry through computer vision? You’re in the right place! Computer vision is one of the most exciting and in-demand fields in the technology industry today. From facial recognition to self-driving cars, it's transforming how machines "see" and understand the world. This guide is crafted to help you learn computer vision from scratch and prepare effectively for tech interviews.

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📌 What is Computer Vision?

Computer Vision is a field of Artificial Intelligence (AI) that trains computers to interpret and understand the visual world. By using images and videos, machines can recognize patterns, detect objects, and even make decisions.

🔍 Real-world examples:

• Face detection in smartphones

• Medical imaging (like detecting tumors)

• Autonomous vehicles

• Surveillance systems

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🎯 Why Learn Computer Vision as a Fresher?

• 📈 High Demand: Computer Vision jobs are booming in industries like healthcare, automotive, retail, and entertainment.

• 💰 Good Pay: Entry-level roles often offer competitive salaries.

• 🧠 Interesting Work: It involves cool projects with real-world applications.

• 🚀 Career Growth: It opens pathways into Data Science, Deep Learning, and AI roles.

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🗺️ Step-by-Step Roadmap to Learn Computer Vision

1. Learn the Prerequisites

Before diving into computer vision, make sure you are comfortable with:

• Python Programming

• Mathematics: Linear Algebra, Probability, and Calculus

• Basic Machine Learning (supervised vs unsupervised learning)

2. Get Started with Image Processing

Learn the basics of how images are stored and processed:

• Pixels, color channels (RGB, grayscale)

• Image transformations (resize, crop, rotate)

• Libraries to learn:

  • 🔧 OpenCV

  • 🐍 Pillow (PIL)

3. Work with Computer Vision Libraries

Start hands-on projects using these tools:

• OpenCV – Basic image and video processing

• scikit-image – For image processing in Python

• MediaPipe – For real-time face/pose detection

• PyTorch or TensorFlow – For deep learning models

4. Dive into Deep Learning for Vision

To build advanced models, learn about:

• Convolutional Neural Networks (CNNs)

• Image Classification

• Object Detection

• Semantic Segmentation

👉 Recommended courses:

• Fast.ai

• DeepLearning.AI on Coursera

5. Build Mini Projects

This is a game-changer for your resume and interviews:

• Face mask detector

• Handwritten digit recognizer (MNIST)

• Object detector using YOLO or SSD

• Real-time face emotion detection

💡 Tip: Host your projects on GitHub and explain them clearly in your README.

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🎤 How to Crack Computer Vision Interviews

📚 Prepare Core Topics

• Difference between image classification and object detection

• How CNNs work

• What is transfer learning?

• Activation functions

• Overfitting vs underfitting

• Popular architectures: ResNet, VGG, MobileNet

🧩 Practice Problem Solving

• Participate in Kaggle computer vision competitions

• Solve problems on platforms like:

  • LeetCode (for DSA + ML-based problems)

  • HackerRank

  • GitHub Issues (open-source contribution)

📂 Create a Portfolio

• Build a simple portfolio website or use GitHub Pages

• Include:

  • About You

  • Projects with live demos/screenshots

  • Blog posts or explanations

  • Resume & Contact Info

💬 Prepare for the Interview Rounds

1. Aptitude + Coding Test – Revise Python + DSA

2. Technical Round – Be ready to explain your projects and approach

3. ML/CV Concepts – They may quiz you on architecture or real-world use-cases

4. HR Round – Showcase your curiosity and learning attitude

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✅ Free Resources to Learn Computer Vision

• Books:

  • "Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani

  • "Make Your Own Neural Network" by Tariq Rashid

• YouTube Channels:

  • Codebasics

  • Sentdex

  • Nicholas Renotte

• Courses:

  • Coursera: Computer Vision Basics

  • Udacity: Computer Vision Nanodegree

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📝 Final Words

Learning Computer Vision as a fresher may seem overwhelming, but if you take small steps and stay consistent, you can master it and impress recruiters. Focus on projects, understanding core concepts, and storytelling during interviews. You don’t need to be perfect; you just need to show you’re passionate and willing to grow.