Artificial Intelligence
From Zero to Agentic AI — One-Year Diploma
Diploma in Artificial Intelligence, Machine Learning & Computer Vision — Python, DSA, ML, Deep Learning, Computer Vision, Generative AI and Agentic AI in 52 weekend weeks.
Level:
Beginner
Duration:
12 Months
100
lessons
0
enrolled
M
INSTRUCTOR
Muhammad Ramzan
Program Structure
Stage 1
Programming Foundations
Weeks 1–13 (13 weeks)
Stage 2
Data Science & Machine Learning
Weeks 14–26 (13 weeks)
Stage 3
Deep Learning & Computer Vision
Weeks 27–39 (13 weeks)
Stage 4
Generative AI, LLMs & Agentic AI
Weeks 40–52 (13 weeks)
What You'll Learn
Write clean Python and solve common data-structure and algorithm problems
Clean, analyse and visualise data, including SQL
Build, evaluate and explain classical ML models — and understand the math behind them
Train deep networks in PyTorch and build classification, detection and segmentation systems
Build and deploy GenAI applications: RAG, fine-tuned small models, and agents using tools and MCP
Show all of it through a public GitHub portfolio and an active LinkedIn profile
About This Course
Many computing students reach their final semesters without a single public project, a GitHub profile, or real confidence in even one programming language — while the technology they will be hired for keeps moving. This program is built to close that gap. Over twelve months of live weekend classes, learners move step by step from their first line of Python to building and deploying Generative and Agentic AI systems, publishing their work on GitHub and LinkedIn along the way.
The sequence is deliberate: each topic builds on the one before it, theory is taught alongside code, and every week ends with a practical task. The goal is simple — graduates who understand what they build and are ready for internships, junior roles, freelancing or research.
Course Curriculum (100 lessons)
2. Sunday · Python Basics
3. Saturday · Control Flow & Problem Solving
4. Sunday · Command Line & Git Basics
5. Saturday · Loops
6. Sunday · Strings
7. Saturday · Functions
8. Sunday · Clean & Correct Code
9. Saturday · Lists & Tuples
10. Sunday · Dictionaries & Sets
11. Saturday · Files & Exceptions
12. Sunday · Modules, Packages & Web APIs
13. Saturday · OOP I
14. Sunday · OOP II
15. Saturday · OOP III
16. Sunday · Pythonic & Tested Code
17. Saturday · Team Git Workflow
18. Sunday · Complexity & Searching
19. Saturday · Sorting
20. Sunday · Recursion & Dynamic Programming
21. Saturday · Linear Data Structures
22. Sunday · Trees & Heaps
23. Saturday · Graphs
24. Sunday · AI Foundations
25. Review — Stage 1 Demo Day
26. Saturday · NumPy
27. Sunday · Linear Algebra I
28. Saturday · Pandas I
29. Sunday · Pandas II
30. Saturday · Data Visualisation
31. Sunday · SQL Essentials
32. Saturday · Probability & Statistics
33. Sunday · Calculus for ML
34. Saturday · ML Fundamentals
35. Sunday · Linear Regression
36. Saturday · Logistic Regression
37. Sunday · Evaluating Classifiers
38. Saturday · KNN & Naive Bayes
39. Sunday · Decision Trees & SVM
40. Saturday · Ensemble Learning
41. Sunday · Professional ML Workflow
42. Saturday · Unsupervised Learning
43. Sunday · Linear Algebra II, PCA & Recommenders
44. Saturday · Text as Data
45. Sunday · Model Deployment I
46. Saturday · Digital Image Processing
47. Sunday · Classical Computer Vision & OCR
48. Saturday · Data Collection & Pipelines
49. Sunday · Docker & Cloud Fundamentals
50. Review — Stage 2 Demo Day
51. Saturday · Neural Networks
52. Sunday · Backpropagation
53. Saturday · PyTorch Fundamentals
54. Sunday · The Training Loop
55. Saturday · Optimisation
56. Sunday · Regularisation & Debugging
57. Saturday · CNN Fundamentals
58. Sunday · CNNs in PyTorch
59. Saturday · CNN Architectures
60. Sunday · Transfer Learning
61. Saturday · Training Like a Professional
62. Sunday · Research & Technical Communication
63. Saturday · Object Detection Concepts
64. Sunday · Object Detection Hands-On
65. Saturday · Real-Time Vision
66. Sunday · Image Segmentation
67. Saturday · Segmentation Hands-On
68. Sunday · Sequence Models
69. Saturday · Attention & Transformers
70. Sunday · Vision Transformers & CLIP
71. Saturday · Hugging Face Ecosystem
72. Sunday · Deploying Models
73. Saturday · Time-Series & Sensor Data
74. Sunday · Reinforcement Learning Essentials
75. Review — Stage 3 Demo Day
76. Saturday · Generative Models
77. Sunday · Diffusion & Image / Video Generation
78. Saturday · How LLMs Work
79. Sunday · Working with LLMs
80. Saturday · Prompt Engineering & Structured Output
81. Sunday · LLM Apps & AI Integration
82. Saturday · Embeddings & Vector Search
83. Sunday · RAG I
84. Saturday · RAG II & Evaluation
85. Sunday · Multimodal & Document AI
86. Saturday · Voice AI
87. Sunday · Fine-Tuning with LoRA
88. Saturday · Agentic AI I
89. Sunday · Agentic AI II
90. Saturday · MCP & Agent Protocols
91. Sunday · Multi-Agent Systems & Automation
92. Saturday · AI-Assisted Software Engineering
93. Sunday · LLMOps & Deployment
94. Saturday · AI Safety, Security & Guardrails
95. Sunday · AI System Design
96. Saturday · Capstone Reviews
97. Sunday · Career I
98. Saturday · Career II
99. Sunday · Pathways
100. Review — Final Demo Day
Practical Tasks & Exercises (48 tasks)
Week 1 Task
Max 10 Points
Week 2 Task
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Week 3 Task
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Week 4 Task
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Week 5 Task
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Week 6 Task
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Week 51 Task
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Capstone Projects (21 projects)
P1 — Smart Electricity Bill Calculator
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P2 — Kiryana POS & Khata (Credit Ledger)
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P3 — Clinic Appointment & Queue System
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P4 — Lahore Metro Route Finder
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P5 — Lahore Smog: Air-Quality Analysis
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P6 — Student At-Risk Early-Warning System
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P7 — Pakistani Used-Car Price Estimator
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P8 — Roman Urdu Feedback Analyzer
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P9 — Receipt & Invoice Digitizer
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P10 — MiniTorch: Your Own Deep-Learning Library
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P11 — Crop-Leaf Disease Detector
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P12 — Motorcycle Helmet-Violation Detector
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P13 — Medical or Satellite Image Segmentation
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P14 — Visual Product Search with CLIP
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P15 — Fruit-Quality Grading Service
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P16 — AI Product-Photo Studio for Online Sellers
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P17 — AI Document-to-JSON Extractor
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P18 — Bilingual Campus Handbook Assistant (RAG)
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P19 — Urdu-English Voice Support Agent
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P20 — AI Shop Assistant Agent with MCP
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P21 — Final Capstone
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Requirements
- A laptop or PC (8 GB RAM recommended) and a stable internet connection
- No GPU needed — free GPU tiers of Google Colab and Kaggle are used
- Free accounts: Google, GitHub, LinkedIn, Kaggle, Hugging Face
- Around 5-6 hours per week for practice and tasks
- No prior programming required; the mathematics is taught inside the program
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PKR 5000.00
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per month · 12 months total
- Interactive Live Classes
- Hands-on Practical Tasks & Reviews
- Real-world Capstone Projects
- Full lifetime access
- Certificate on completion
- Access on all devices
- Community forum access