After completing the 30 Days ML and DL challenges, the time has come to shift focus from day-long tasks to longer, more complex projects where I can dive deeper into each topic.
I have designed this 4-month learning plan (of course with the help of ChatGPT) to help in going deeper in the AI and ML world with intensive weeklong to biweekly projects. It includes practical, hands-on tasks, cutting-edge topics, and real-world applications. The plan focuses on:
- Computer Vision Projects to deepen expertise in image processing.
- Recommender Systems and Reinforcement Learning for practical and industry-ready skills.
- Generative and Transformer Models for exploring experimental, cutting-edge topics.
- Capstone Projects to combine skills into impactful applications.
Month 1: Computer Vision Focus 🎯
#| Week(s) | Project Title | Key Focus Areas |
|---|
| 1–2 | Build an Advanced Object Detection System | - YOLOv5 or Faster R-CNN- Custom dataset for vehicle or pedestrian detection |
| 3–4 | Image Segmentation with U-Net | - Semantic segmentation on medical or urban datasets |
Month 2: Generative Models and Transformers ✨
#| Week(s) | Project Title | Key Focus Areas |
|---|
| 5–6 | Style Transfer and Artistic AI | - Neural Style Transfer or CycleGAN for artistic applications |
| 7–8 | Build a Custom Vision Transformer (ViT) | - Vision Transformer for image classification |
Month 3: Recommender Systems and Reinforcement Learning 🎮
#| Week(s) | Project Title | Key Focus Areas |
|---|
| 9–10 | Build a Personalized Recommender System | - Use embeddings from transformers or collaborative filtering- Deploy as a web app |
| 11–12 | Reinforcement Learning for Game AI | - Train an RL agent for a game like Pong or CartPole- Implement DQN or PPO |
Month 4: Capstone and Cutting-Edge AI Projects ✨
#| Week(s) | Project Title | Key Focus Areas |
|---|
| 13–14 | Generative Models with StyleGAN2 | - StyleGAN2 for high-quality image synthesis |
| 15–16 | Capstone Project: Your Vision | - Combine techniques to build a meaningful application- Example: Smart surveillance, AI art app |
How to Approach This Plan 📅
#- Time Commitment: Each project is designed for 1–2 hours daily over 1–2 weeks.
- Daily Workflow:
- Week 1:
- Day 1–2: Research (papers, blogs, and tutorials) and dataset setup.
- Day 3–4: Design the architecture or adapt a pre-trained model.
- Day 5: Train the base model (test on small data subsets first).
- Day 6–7: Evaluate and analyze initial results.
- Week 2:
- Day 8–9: Improve performance (hyperparameter tuning, data augmentation).
- Day 10: Test variations or implement additional features.
- Day 11–12: Finalize model and document results (visualizations, write-up).
- Day 13–14: Make YouTube video on the project.
Follow Along!
#Follow my journey as I dive deeper into AI/ML, and feel free to reach out or comment with questions, suggestions, or your own project ideas! Don’t forget to check out my YouTube channel for regular updates.
