Dear @g15713,
Excellent. Please proceed as planned.
–
Keep Learning AI with DeepLearning.AI - Girijesh
AI & ML Learning Roadmap (15-Month Core + Optional PyTorch Track)
Copilot says: “Use this if you want to show a strong foundation in AI/ML, with room to pivot into NLP, time series, or deployment. Why it works: Signals versatility, aligns with math + ML coursework, and attracts recruiters seeking adaptable talent. Bonus: You can always tailor a CV-specific subset if a job calls for it.”
| Stage | Course | Duration | Purpose |
|---|---|---|---|
| 1 | AI Python for Beginners | ~1 month | Brush up on Python essentials |
| 2 | Mathematics for ML & Data Science | ~3 months | Build math foundation |
| 3 | Machine Learning (Andrew Ng) | ~2 months | Core ML theory, supervised learning, regularization, etc. |
| 4 | Deep Learning Specialization | ~3 months | Neural networks, CNNs, RNNs, sequence models |
| 5 | TensorFlow Developer Certificate | ~2 months | Hands-on TensorFlow, computer vision, NLP, time series |
| 5A | PyTorch for Deep Learning (optional) | ~2 months | PyTorch fundamentals, tensors, autograd, training loops |
| 6 | TensorFlow: Advanced Techniques | ~2 months | Custom models, segmentation, generative AI |
| 6A | Advanced PyTorch (optional) | ~2 months | Custom modules, transfer learning, GANs, optimization |
| 7 | TensorFlow: Data and Deployment | ~2 months | Model deployment, data pipelines, mobile integration |
| 7A | PyTorch Deployment & Scaling (optional) | ~2 months | TorchServe, ONNX, mobile deployment, production workflows |
Hello Charles @g15713 , what are some CV tasks that you want to be able to do and to what extent?
Copilot (My Coach) helped me with the question:
CV Goals (Computer Vision)
Short-Term:
-
Build foundational CV skills through coursework and hands-on projects
-
Explore image classification, object detection, and segmentation using TensorFlow and PyTorch
-
Understand key architectures like CNNs, ResNet, and U-Net
Mid-Term (1 Year+):
-
Apply CV techniques to real-world domains—especially medical imaging, where precision and reproducibility matter
-
Develop models for tasks like anomaly detection, image enhancement, or diagnostic support
There was a suggestion from my Coach to take AI for Medicine Specialization
I said to my Coach you are a career advisor, reply was - “Absolutely—I’m your career advisor, coach, and sounding board rolled into one.”
I see. So you are partnering with Copilot as your coach for your learning journey, and I totally respect that ;). Then I would suggest you to perhaps also ask it “what are the essential elements for a good learning roadmap?” I tried and among the answer, I find “assessment and feedback” to be particularly important and so far missing from your roadmap. Perhaps you might explore more with your coach down the road. Good luck to you Charles @g15713 
Raymond
Again, Copilot (My Coach) helped me with the question:
Essential Elements of a Good Learning Roadmap
-
Clear Objective Define the end goal: career pivot, certification, project delivery, or domain mastery.
-
Modular Structure Break learning into stages (e.g., Python → Math → ML → Deployment) with logical progression.
-
Time Estimates Assign realistic durations to each module to maintain momentum and track progress.
-
Skill Alignment Match each stage to specific skills or tools (e.g., CNNs for CV, Pandas for data wrangling).
-
Project Milestones Include hands-on projects to reinforce learning and build portfolio artifacts.
-
Optional Tracks Offer flexibility (e.g., PyTorch vs. TensorFlow) to adapt to evolving interests or job targets.
-
Feedback & Reflection Build in checkpoints for review, iteration, and mentorship—just like QA cycles.
-
Outcome Mapping Tie each phase to a tangible outcome: resume bullet, GitHub repo, certification, or job role.
My Coach is Great! 
Your Career Pivot Strategy
Legacy Strengths
-
15+ years in QA and engineering at Motorola
-
Deep process rigor, calibration, and reproducibility
-
Strong documentation, mentorship, and technical troubleshooting
Current Momentum
-
Completed Python and math foundations
-
Building a modular AI/ML roadmap with TensorFlow and optional PyTorch
-
Exploring CV and medical imaging with a focus on reproducible workflows
Next Moves
-
Add the AI for Medicine Specialization to deepen healthcare relevance
-
Build a flagship project (e.g., reproducible lung X-ray classifier with QA-style metrics)
-
Target remote, part-time roles in healthcare AI, diagnostics, or research support
-
Position yourself as a mentor and technical communicator in emerging tech
Hi,
I think there’s an issue with the list—it only includes courses on ML/DL. To get a more complete understanding of computer vision, it’s important to also include courses that cover traditional approaches, not just DL. Personally, I found Udacity’s Introduction to Computer Vision quite helpful for that. (I’m not affiliated with Udacity, just sharing my experience.)
Apparently there were no Udacity courses in the training set used by Copilot. 
I probably told Copilot to only include a list from Coursera
Here is a response from Copilot:
“Hi Charles,
You’re absolutely right—and I appreciate you calling that out. A well-rounded understanding of computer vision should absolutely include traditional techniques like:”
-
Feature detection and matching (e.g., SIFT, SURF, ORB) -
Classical image processing (e.g., filtering, edge detection, segmentation)
-
Geometric vision (e.g., camera models, epipolar geometry, stereo vision) -
Structure from motion and SLAM (especially relevant for robotics and AR)
“Deep learning dominates the current landscape, but many foundational CV tasks still benefit from traditional methods—especially when interpretability, speed, or limited data are factors.”
Balanced Course Recommendations
“Here’s a more holistic list that blends classical and DL-based approaches:”
| Course | Platform | Focus Area |
|---|---|---|
| Introduction to Computer Vision | Udacity | Classical CV: filters, features, camera geometry |
| Computer Vision Basics | Coursera (University at Buffalo) | Classical methods with OpenCV |
| Image and Video Processing | Coursera (Duke University) | Signal processing foundations |
| CS231n: CNNs for Visual Recognition | Stanford (YouTube + notes) | Deep learning for CV |
| Deep Learning for Computer Vision | Coursera (National Research University) | DL-heavy, but includes some classical context |
| OpenCV Tutorials | OpenCV.org | Hands-on classical techniques |