Beginner → Intermediate (Student & Self-Learner Friendly) A practical, visual roadmap for learning AI & Machine Learning, focused on Computer Vision, real projects, and thesis-ready skills.
👋 Who This Roadmap Is For
🎓 IT / CS students
👨💻 Beginner to intermediate developers
🧠 Self-learners entering AI & ML
📘 Thesis & capstone project builders
🧭 How to Use This Roadmap
Follow top → bottom
Skip sections you already know
Each level has clear goals
Repositories are linked for hands-on practice
🟢 LEVEL 0 — Prerequisites (Must Know)
Skip if you already know these
🐍 Python Basics
Variables, loops, functions
Lists, dictionaries
Virtual environments
📐 Light Math (Enough lang)
Mean, variance
Basic linear algebra idea
What gradients mean (conceptual)
🟢 LEVEL 1 — Foundations of AI & ML
Goal: Understand how machines “learn”
🧠 Core Concepts
What is Machine Learning?
Supervised vs Unsupervised learning
Training vs Testing
Overfitting & Underfitting
✔ Output: You can explain ML in simple words
🟡 LEVEL 2 — Computer Vision Basics
Goal: Understand images as data
📷 Image Fundamentals
Pixels & channels
Grayscale vs RGB
Image resolution
🛠 OpenCV Basics
Reading images
Resizing
Grayscale conversion
🔗 Practice Repo: 👉 opencv-face-preprocessing
🟡 LEVEL 3 — Data Preprocessing (VERY IMPORTANT)
Goal: Prepare clean data for ML models
🧹 Preprocessing Steps
Face detection
Image resizing
Normalization
Dataset organization
Mixed image sizes
No preprocessing
Dirty datasets
🔗 Practice Repo: 👉 opencv-face-preprocessing
🔵 LEVEL 4 — Machine Learning Models
Goal: Train models on image data
🤖 Convolutional Neural Networks (CNN)
What CNNs are
Feature extraction
Why CNNs work well for images
📈 Support Vector Machine (SVM)
What SVM is
When SVM is better than CNN
Small dataset advantage
Model Difficulty Use Case CNN Medium Large image datasets SVM Medium Smaller, clean datasets 🔵 LEVEL 5 — Model Evaluation
Goal: Know if your model is actually good
📊 Evaluation Metrics
Accuracy
Precision
Recall
Confusion Matrix
High accuracy ≠ good model
🔴 LEVEL 6 — Hybrid Models (Intermediate)
Goal: Combine strengths of multiple models
🔥 Hybrid CNN–SVM
CNN as feature extractor
SVM as classifier
Better generalization
🧠 Real-world use:
Face classification
Emotion detection
Intoxication detection
🔗 Related Project: 👉 cnn-svm-hybrid-face-classifier (coming soon)
🔴 LEVEL 7 — Dataset, Ethics & Reality Checks
Goal: Think like a real AI engineer
Lighting bias
Pose variations
Imbalanced classes
⚖️ Ethics & Privacy
Face data sensitivity
Consent & legality
Responsible AI usage
🎓 LEVEL 8 — Thesis & Capstone Ready
Goal: Turn learning into academic & portfolio success
📘 Thesis Tips
Dataset justification
Algorithm comparison
Panel defense readiness
💼 Portfolio Tips
Clean README
Clear problem statement
Reproducible results
🧰 Recommended Tools Category Tools Language Python CV OpenCV ML TensorFlow, Scikit-learn Data NumPy, Pandas Visualization Matplotlib 🚀 What To Do Next
1️⃣ Start with opencv-face-preprocessing 2️⃣ Learn CNN basics 3️⃣ Try SVM 4️⃣ Build a Hybrid model 5️⃣ Prepare for thesis or portfolio
📌 Author
Ares Coding AI & Software Developer GitHub: https://github.com/ares-coding
📜 License
MIT License — free to use with attribution.