School Teacher Break Into Deep Learning 2026

Phase 1 — Daily Schedule (Months 1-3):
6:00 AM — Wake up, exercise (30 min)
6:30 AM — Review goals for the day
7:00 AM — Deep work (1.5 hours)
   - Week 1-2: Python basics
   - Week 3-4: Python libraries
   - Week 5-6: Math for ML
   - Week 7-8: Statistics
   - Week 9-10: Data wrangling
   - Week 11-12: Git & GitHub
8:30 AM — Break
9:00 AM — Practice (1 hour)
   - Coding exercises, small projects
10:00 AM — Review & plan tomorrow
10:30 AM — Prepare for school / commute
4:00 PM — School ends
5:00 PM — Rest / personal time
7:00 PM — Skill building (1 hour)
   - Read docs, watch tutorials, practice
9:00 PM — Wind down

Week 1-2 Review:
□ Do I understand Python syntax and data structures?
□ Can I write functions and use OOP?
□ Have I solved 20+ coding exercises?

Week 3-4 Review:
□ Can I use NumPy and Pandas?
□ Can I create visualisations with Matplotlib?
□ Have I built a data analysis notebook?

Week 5-6 Review:
□ Do I understand linear algebra basics?
□ Do I understand derivatives and probability?
□ Can I apply math concepts to ML problems?

Week 7-8 Review:
□ Do I understand descriptive statistics?
□ Can I perform hypothesis testing?
□ Have I analysed a real dataset?

Week 9-10 Review:
□ Can I clean messy data?
□ Can I visualise data effectively?
□ Have I completed a data cleaning project?

Week 11-12 Review:
□ Do I understand Git basics?
□ Are all my practice files on GitHub?
□ Can I create branches and commits?

Phase 2 — Daily Schedule (Months 4-8):
6:00 AM — Wake up, exercise (30 min)
6:30 AM — Review goals for the day
7:00 AM — Deep work (1.5 hours)
   - Week 13-16: Machine learning
   - Week 17-20: Neural networks
   - Week 21-24: CNNs & CV
   - Week 25-28: RNNs & NLP
   - Week 29-32: Transformers
8:30 AM — Break
9:00 AM — Practice (1 hour)
   - Implement models, solve exercises
10:00 AM — Review & plan tomorrow
10:30 AM — Prepare for school / commute
4:00 PM — School ends
5:00 PM — Rest / personal time
7:00 PM — Skill building (1 hour)
   - Read papers, watch tutorials, experiment
9:00 PM — Wind down

Week 13-16 Review:
□ Do I understand supervised vs unsupervised learning?
□ Can I implement regression and classification?
□ Have I used scikit-learn?

Week 17-20 Review:
□ Do I understand backpropagation?
□ Can I build a neural network from scratch?
□ Do I understand activation functions?

Week 21-24 Review:
□ Do I understand CNNs?
□ Can I use transfer learning?
□ Have I built an image classifier?

Week 25-28 Review:
□ Do I understand RNNs and LSTMs?
□ Can I build a text classifier?
□ Have I used embeddings?

Week 29-32 Review:
□ Do I understand attention mechanisms?
□ Can I fine-tune a transformer?
□ Have I experimented with Hugging Face?

Phase 3 — Daily Schedule (Months 9-12):
6:00 AM — Wake up, exercise (30 min)
6:30 AM — Review goals for the day
7:00 AM — Deep work (1.5 hours)
   - Week 33-36: Portfolio projects
   - Week 37-40: Deployment
   - Week 41-44: Resume & applications
   - Week 45-48: Interview prep
8:30 AM — Break
9:00 AM — Practice / applications (1 hour)
10:00 AM — Review & plan tomorrow
10:30 AM — Prepare for school / commute
4:00 PM — School ends
5:00 PM — Rest / personal time
7:00 PM — Interview prep (1 hour)
   - Practice questions, review projects
9:00 PM — Wind down

Week 33-36 Review:
□ Have I built 3 substantial DL projects?
□ Do they cover different domains (CV, NLP)?
□ Are they documented with clear READMEs?

Week 37-40 Review:
□ Have I deployed 2 projects?
□ Do I understand Docker basics?
□ Can I serve a model with FastAPI?

Week 41-44 Review:
□ Is my resume updated for DL roles?
□ Does it highlight teaching + DL skills?
□ Have I applied to 25 jobs?

Week 45-48 Review:
□ Have I practiced ML theory questions?
□ Have I practiced coding challenges?
□ Have I done 10 mock interviews?