Welcome to the 90-Day AI Upskilling Program — a structured, hands-on path designed for professionals coming from traditional languages like .NET (C#), C++ MFC, and FORTRAN who now want to enter the AI field using Python and real-world projects.
This repository will guide you through all the necessary tools, programming concepts, and AI/ML practices over 90 days, one day at a time. Each day comes with:
- 📚 Theory (with beginner-friendly explanations)
- 💻 Practical coding exercises
- 📓 Jupyter notebooks
- ✅ Real-world mini projects
- ☁️ Deployment, GitHub versioning, and portfolio building
💡 All daily lessons are kept in the
docs/folder for easy access.
AI-90Days/
│
├── Day1_Setup/ # Your code, notebooks, and files for Day 1
├── Day2_PythonBasics/ # Folder for Day 2 exercises and code
├── ...
├── docs/ # Contains markdown files for each day
│ ├── Day1.md # Full guide for Day 1
│ ├── Day2.md # Full guide for Day 2
│ └── ...
│
└── README.md # This overview file
Each link below takes you to the detailed tutorial and instructions for that day.
| Day | Topic | Link |
|---|---|---|
| 1 | Tools Setup + Git + Jupyter Intro | Day 1 |
| 2 | Python Basics | Day 2 |
| 3 | Collections: Lists, Tuples, Sets & Dictionaries | Day 3 |
| 4 | Control Flow: If, For, While, and Logic | Day 4 |
| 5 | Functions | Day 5 |
| 6 | Modules and Packages | Day 6 |
| 7 | Exception Handling | Day 7 |
| 8 | File Handling | Day 8 |
| 9 | Working with CSV and JSON Files | Day 9 |
| 10 | NumPy for AI | Day 10 |
| 11 | Pandas for Data Analysis | Day 11 |
| 12 | Data Cleaning and Feature Engineering | Day 12 |
| 13 | Data Visualization with Matplotlib & Seaborn | Day 13 |
| 14 | Exploratory Data Analysis (EDA) | Day 14 |
| 15 | Machine Learning with Scikit-learn | Day 15 |
| 16 | Data Preprocessing and Pipelines | Day 16 |
| 17 | Linear Regression | Day 17 |
| 18 | Logistic Regression & Classification Metrics | Day 18 |
| 19 | Decision Trees & Entropy | Day 19 |
| ... | ... | ... |
| 90 | Final Project & Portfolio Deployment | Coming Soon |
✅ Links will be updated here each day as you progress.
By the end of 90 days, you'll:
- Be proficient in Python
- Understand core AI/ML concepts
- Build deployable real-world projects
- Gain Git/GitHub portfolio management skills
- Be ready to apply for AI-related roles confidently
- Python 3.11+
- Visual Studio 2022 (with Python workload)
- Jupyter Notebooks
- Git & GitHub Desktop
- ML Libraries: scikit-learn, pandas, matplotlib, TensorFlow (later)
This journey is open-source. If you're following along or want to contribute fixes or translations, feel free to fork the repo and send pull requests!
Happy Learning! 🚀