Top Deep Learning Classes Near Delhi Compared for 2026

Month 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 1: Python and NumPy
   - Week 2: Pandas and data prep
   - Week 3: ML fundamentals
   - Week 4: Scikit-learn
12:00 PM — Lunch break
1:00 PM — Practice (Kaggle, build models)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Do I understand Python syntax?
□ Can I use NumPy arrays?
□ Have I written 10 scripts?

Week 2 Review:
□ Can I load data with Pandas?
□ Can I clean a dataset?
□ Have I engineered features?

Week 3 Review:
□ Do I understand regression?
□ Do I understand classification?
□ Have I trained a baseline model?

Week 4 Review:
□ Can I evaluate a model?
□ Do I understand cross-validation?
□ Have I used Scikit-learn?

Month 2 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 5: Neural network basics
   - Week 6: TensorFlow and Keras
   - Week 7: CNNs and computer vision
   - Week 8: PyTorch and Project 1
12:00 PM — Lunch break
1:00 PM — Practice (train models, build CNNs)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Do I understand perceptrons?
□ Do I understand backpropagation?
□ Have I built a simple ANN?

Week 6 Review:
□ Can I build a Keras model?
□ Can I compile and fit?
□ Have I evaluated a model?

Week 7 Review:
□ Do I understand convolution?
□ Can I use transfer learning?
□ Have I built an image classifier?

Week 8 Review:
□ Can I use PyTorch?
□ Is Project 1 complete?
□ Is it on GitHub?

Month 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 9: Transformers and LLMs
   - Week 10: RAG and LangChain
   - Week 11: MLOps and deployment
   - Week 12: Interview prep and applications
12:00 PM — Lunch break
1:00 PM — Networking and follow-ups
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Interview prep (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Do I understand attention?
□ Can I use Hugging Face?
□ Have I fine-tuned a model?

Week 10 Review:
□ Do I understand RAG?
□ Can I use LangChain?
□ Have I built a RAG pipeline?

Week 11 Review:
□ Can I build a FastAPI endpoint?
□ Can I use Docker?
□ Have I deployed a model?

Week 12 Review:
□ Have I done mock interviews?
□ Have I sent 25 quality applications?
□ Have I followed up?

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Deep work session 1 (3 hours)
☐ Deep work session 2 (2 hours)
☐ Practice exercises completed
☐ Project work completed
☐ Exercise completed
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

Weekly Tracker:
Week: _____ (Month 1 / 2 / 3)

Goals for this week:
1. 
2. 
3. 

Daily Progress:
Mon: ☐☐☐
Tue: ☐☐☐
Wed: ☐☐☐
Thu: ☐☐☐
Fri: ☐☐☐
Sat: ☐☐☐
Sun: ☐☐☐

Weekly Wins:
1. 
2. 
3. 

Challenges & Solutions:
1. 
2. 

Next Week's Goals:
1. 
2. 
3. 

Monthly Tracker:
Month: _____ (1 / 2 / 3)

Monthly Goal:
_______________________________________________

Week 1 Progress: ☐☐☐
Week 2 Progress: ☐☐☐
Week 3 Progress: ☐☐☐
Week 4 Progress: ☐☐☐

Monthly Wins:
1. 
2. 
3. 

Skills Gained:
1. 
2. 
3. 

Portfolio Items:
1. 
2. 
3. 

Applications Sent: _____
Interviews: _____
Offers: _____

Next Month's Goal:
_______________________________________________