ML-Roadmap-and-Notes
A carefully curated collection of machine learning notes, resources, projects, and datasets designed to guide you through the ML landscape effectively.
File Explorer
- Basic Python .ipynb
- OOPS in Python.ipynb
- 1. File Handling.ipynb
- 2. Exception Handling.ipynb
- 3. Regular Expressions.ipynb
- 4. Functional Programming.ipynb
- Exercise.ipynb
- 100_Numpy_exercises.ipynb
- Broadcasting.png
- Numpy.ipynb
- 100-pandas-puzzles.ipynb
- deliveries.csv
- Fortune500.csv
- Learn Pandas.ipynb
- matches.csv
- train.csv
- Matplotlib .ipynb
- Boxplot.png
- Boxplot1.png
- Seaborn.ipynb
- Seaborn.png
- Violinplots.png
- Fundamentals of Statistics.ipynb
- README.md
- Learn Data Analysis Process.ipynb
- Learn Exploratory Data Analysis (EDA).ipynb
- Learn Machine Learning Basics.ipynb
- Tensors.ipynb
- Advanced Statistics.ipynb
- README.md
- Fundamentals of Probability.ipynb
- Fundamentals of Linear Algebra.ipynb
- README.md
- Fundamentals of Calculus.ipynb
- AdaBoost Note.ipynb
- Bagging.ipynb
- bagging_demo.ipynb
- DBSCAN Clustering.ipynb
- Decision Tree Classification Demo.ipynb
- Decision Tree Notes.ipynb
- dtreeviz_demo.ipynb
- Gradient Boosting.ipynb
- Gradient Descent.ipynb
- Hierarchical Clustering.ipynb
- K Nearest Neighbors.ipynb
- K_Means .ipynb
- Linear Regression.ipynb
- Logistic Regression.ipynb
- Naive Bayes.ipynb
- PCA.ipynb
- Random Forest.ipynb
- Kernel Trick SVM.ipynb
- Support Vector Machines .ipynb
- XGBoost.ipynb
- Machine Learning All Models.md
- Machine Learning Metrics.ipynb
- Regularization.ipynb
- Data Acquisition.ipynb
- Feature Selection.ipynb
- Cross Validation.ipynb
- Blending.ipynb
- CatBoost.ipynb
- LightGBM.ipynb
- Stacking.ipynb
- GridSearchCV .ipynb
- Hyperparameter Tuning.ipynb
- RandomSearchCV.ipynb
- Handling imbalanced dataset.ipynb
- Handling Multicollinearity.ipynb
- Data Leakage.ipynb
- Working with Large Datasets.ipynb
- Complete Case Analysis.ipynb
- Handling missing numerical data.ipynb
- Handling missing categorical data.ipynb
- Missing indicator.ipynb
- KNN Imputer.ipynb
- MICE.ipynb
- Feature Scaling and Normalization.ipynb
- Feature Encoding Techniques.ipynb
- FunctionTransformer.ipynb
- Power Transformations.ipynb
- Binning and Binarization.ipynb
- Column Transformer.ipynb
- Scikit-Learn Pipelines.ipynb
- Working with time and date data .ipynb
- Working with Outliers.ipynb
- Feature Construction.ipynb
- AI_ML Cheat Sheet .pdf
- Essential Math for Data Science (Thomas Nield) (Z-Library).pdf
- README.md
- Top Resources to Learn Machine Learning in 2025.pdf
- README.md
# Use via CDN
jsDelivrjsDelivr serves any public GitHub repository as a CDN with zero setup. Pick a version and a file to get a ready-to-paste link and snippet.
Command Glossary
Commands referenced in this DOCs, explained below.
mypy
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mypy
Type check Python code.
mypy {{path/to/file.py}}
Type check a specific file:
mypy {{[-m|--module]}} {{module_name}}
Type check a specific module:
mypy {{[-p|--package]}} {{package_name}}
Type check a specific package:
poetry
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poetry
Manage Python packages and dependencies.
Some subcommands such as `about`, `check`, `env`, etc. have their own usage documentation.
See also: `asdf`, `pipenv`, `hatch`.
poetry new {{project_name}}
Create a new Poetry project in the directory with a specific name:
poetry add {{dependency}}
Install and add a dependency and its sub-dependencies to the `pyproject.toml` file in the current directory:
poetry install
Install the project dependencies using the `pyproject.toml` file in the current directory:
pytest
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pytest
Run Python tests.
pytest {{path/to/test_file1.py path/to/test_file2.py ...}}
Run tests from specific files:
pytest -k {{expression}}
Run tests with names matching a specific [k]eyword expression:
pytest {{[-x|--exitfirst]}}
Exit as soon as a test fails or encounters an error:
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