https://github.com/waqasm78/ai-90days
Learn AI in 90 Days – A Complete Beginner-to-Project Guide with Python, Jupyter & Visual Studio 2022
https://github.com/waqasm78/ai-90days
ai beginner-friendly beginner-friendly-project jupyter-notebook learning-path machine-learning python visual-studio
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Learn AI in 90 Days – A Complete Beginner-to-Project Guide with Python, Jupyter & Visual Studio 2022
- Host: GitHub
- URL: https://github.com/waqasm78/ai-90days
- Owner: waqasm78
- Created: 2025-06-26T00:20:11.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-26T20:00:04.000Z (12 months ago)
- Last Synced: 2025-06-26T20:38:40.574Z (12 months ago)
- Topics: ai, beginner-friendly, beginner-friendly-project, jupyter-notebook, learning-path, machine-learning, python, visual-studio
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 90-Day AI Learning Journey 🚀
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/`](docs/README.md) folder for easy access.
---
## 🗂 Folder Structure
```
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
```
---
## 🔗 Daily Learning Modules
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](docs/Day1_Setup.md) |
| 2 | Python Basics | [Day 2](docs/Day2_PythonBasics.md) |
| 3 | Collections: Lists, Tuples, Sets & Dictionaries | [Day 3](docs/Day3_Collections.md) |
| 4 | Control Flow: If, For, While, and Logic | [Day 4](docs/Day4_ControlFlow.md) |
| 5 | Functions | [Day 5](docs/Day5_Functions.md) |
| 6 | Modules and Packages | [Day 6](docs/Day6_Modules.md) |
| 7 | Exception Handling | [Day 7](docs/Day7_Exceptions.md) |
| 8 | File Handling | [Day 8](docs/Day8_FileHandling.md) |
| 9 | Working with CSV and JSON Files | [Day 9](docs/Day9_DataFiles.md) |
| 10 | NumPy for AI | [Day 10](docs/Day10_NumPyBasics.md) |
| 11 | Pandas for Data Analysis | [Day 11](docs/Day11_PandasBasics.md) |
| 12 | Data Cleaning and Feature Engineering | [Day 12](docs/Day12_Data_Cleaning.md) |
| 13 | Data Visualization with Matplotlib & Seaborn | [Day 13](docs/Day13_Data_Visualization.md) |
| 14 | Exploratory Data Analysis (EDA) | [Day 14](docs/Day14_EDA.md) |
| 15 | Machine Learning with Scikit-learn | [Day 15](docs/Day15_Machine_Learning.md) |
| 16 | Data Preprocessing and Pipelines | [Day 16](docs/Day16_DataPipeline.md) |
| 17 | Linear Regression | [Day 17](docs/Day17_LinearRegression.md) |
| 18 | Logistic Regression & Classification Metrics | [Day 18](docs/Day18_LogisticRegression.md) |
| 19 | Decision Trees & Entropy | [Day 19](docs/Day19_DecisionTrees.md) |
| ... | ... | ... |
| 90 | Final Project & Portfolio Deployment | Coming Soon |
> ✅ Links will be updated here each day as you progress.
---
## 🧠 Why This Journey?
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
---
## 🛠 Tools Used
* Python 3.11+
* Visual Studio 2022 (with Python workload)
* Jupyter Notebooks
* Git & GitHub Desktop
* ML Libraries: scikit-learn, pandas, matplotlib, TensorFlow (later)
---
## 📬 Questions / Contributions
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! 🚀