My personal comprehensive course and notes on Machine Learning, Deep Learning, and Data Science — from Python fundamentals and math foundations to Transformers, Vision models, and Transfer Learning with live in-browser Python execution.
👉 https://muhammadanas20.github.io/Machine-Learning/
| Week | Section / Topic | Notebook | Datasets |
|---|---|---|---|
| Week 01 | Python Programming Basics | Week01_Python_Fundamentals.ipynb |
— |
| Week 02 | Data Science Essentials (NumPy, Pandas, Seaborn) | Week02_NumPy_Pandas_Visualization.ipynb |
— |
| Week 03 | Mathematics for Machine Learning (Linear Algebra, Calculus) | Week03_Math_for_ML.ipynb |
— |
| Week 04 | Applied Probability, Statistics & Regression | Week04_Applied_Probability_Statistics_Regression.ipynb |
— |
| Week 05 | Introduction to Machine Learning (Linear/Logistic Reg, Trees) | Week05_Train_Test_Regression_Classification.ipynb |
Telco Churn |
| Week 06 | Feature Engineering & Pipelines (Encoding, Scaling, Selection) | Week06_Feature_Engineering.ipynb |
Bike Sharing |
| Week 07 | Advanced ML & Ensemble Methods (Random Forest, XGBoost) | Week07_Ensemble_Methods.ipynb |
Telco Churn |
| Week 08 | Model Tuning & Optimization (GridSearch, Optuna, CV) | Week08_Hyperparameter_Tuning.ipynb |
Telco Full |
| Week 09 | Deep Learning Foundations (ANN, Backprop, TensorFlow/PyTorch) | Week09_Deep_Learning_Foundations.ipynb |
— |
| Week 10 | Convolutional Neural Networks (CNNs, ResNet, Augmentation) | Week10_CNNs_In_Depth.ipynb |
— |
| Week 11 | Recurrent Neural Networks (SimpleRNN, LSTM, GRU, Seq2Seq) | Week11_RNN_LSTM_GRU.ipynb |
— |
| Week 12 | Transformers & Attention Mechanisms (Self-Attention, BERT, GPT) | Week12_Transformers_Attention_BERT_GPT.ipynb |
— |
| Week 13 | Transfer Learning & Fine-Tuning (ViT, LoRA, Capstone) | Week13_Transfer_Learning_Fine_Tuning.ipynb |
— |
Total: 13 Structured Weeks | 350+ Interactive Cells | 3 Real Datasets | Math Reference Book
- 📱 Installable Progressive Web App (PWA) — installable on mobile Chrome / Android / iOS and desktop. Includes a web app manifest, app icons, a service worker for offline support, an in-app Install App prompt, and a mobile bottom navigation bar.
- ⚡ Offline-ready — once visited, the app shell, notebooks, datasets, and math PDF are cached by a service worker so you can keep learning with no connection.
- 🐍 Pyodide Python WASM Runtime — run Python code cells live inside your browser without any server backend. Pre-loaded with
NumPy,Pandas,Matplotlib, andScikit-Learn. - 📊 Matplotlib Chart Generation — code cells automatically capture
plt.show()and render high-resolution PNG plots directly below the cell. - 📁 Course Datasets Explorer — inspect, preview, search, download, and load real CSV datasets (
Telco-Customer-Churn.csv,bike_sharing_daily.csv,Telco-Customer-Churn-Full.csv) directly into the Python sandbox. - 📖 Jupyter Notebook Viewer — renders
.ipynbcells dynamically with Markdown parsing and KaTeX LaTeX math equation rendering ($...$ ,$$...$$ ). - 📚 Math Reference Book — built-in viewer for
Machine learning maths book.pdf. - ⚡ 1-Click Google Colab & GitHub Launchers — launch any notebook instantly on Colab or inspect on GitHub.
- 🔍 Instant Search — search across all 13 modules, topics, models, and datasets.
- ❤️ Bookmarks & Progress Tracker — save favorite notebooks and mark completed weeks to track progress.
- 🌙 Neumorphic Dark / Light Mode — modern UI design in dark and light themes.
- Open the Live Webpage.
- Select any Week from the course roadmap sidebar or overview grid.
- Read notebook content and math formulas rendered inline.
- Click Run Cell on any Python code block to execute code live in Pyodide.
- Click Datasets in top header to explore course CSV files or Python Sandbox to experiment freely.
Machine-Learning/
├── index.html ← Interactive Web App
├── manifest.webmanifest ← PWA manifest (installable app)
├── sw.js ← Service worker (offline support)
├── icons/ ← PWA app icons (192/512, Apple, maskable)
├── Machine learning maths book.pdf ← Core Math Reference PDF
├── Section_02_Week_01_Python_Programming_Basics/ ← Python Fundamentals
├── Section_03_Week_02_Data_Science_Essentials/ ← NumPy & Pandas
├── Section_04_Week_03_Mathematics_for_Machine_Learning/ ← Linear Algebra & Calculus
├── Section_05_Week_04_Probability_and_Statistics/ ← Stats & Regression
├── Section_06_Week_05_Introduction_to_Machine_Learning/ ← Telco Churn Dataset
├── Section_07_Week_06_Feature_Engineering_and_Model_Evaluation/ ← Bike Sharing Dataset
├── Section_08_Week_07_Advanced_Machine_Learning_Algorithms/ ← Ensemble Methods
├── Section_09_Week_08_Model_Tuning_and_Optimization/ ← Hyperparameter Tuning
├── Section_10_Week_09_Neural_Networks_and_Deep_Learning/ ← ANN Foundations
├── Section_11_Week_10_Convolutional_Neural_Networks/ ← CNN Architectures
├── Section_12_Week_11_Recurrent_Neural_Networks/ ← LSTM & GRU
├── Section_13_Week_12_Transformers_and_Attention/ ← BERT & GPT
└── Section_14_Week_13_Transfer_Learning_and_Fine_Tuning/ ← Fine-Tuning & Capstone
- Python Runtime: Pyodide WebAssembly (Python 3.11 WASM) + Judge0 CE API fallback
- Frontend Core: HTML5 + CSS3 + Vanilla JavaScript
- Styling: Tailwind CSS v4 + Custom Neumorphic Token System
- Markdown & Math: marked.js + KaTeX
- Syntax Highlighting: highlight.js (Tokyo Night Dark)
Built with ❤️ for Machine Learning & Deep Learning Mastery