References & Further Reading

Books

  1. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media.

  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

  3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.

  4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Online Resources

Documentation

  • Scikit-learn Documentation: https://scikit-learn.org/stable/
  • XGBoost Documentation: https://xgboost.readthedocs.io/
  • Pandas Documentation: https://pandas.pydata.org/docs/
  • Matplotlib Documentation: https://matplotlib.org/stable/contents.html
  • Seaborn Documentation: https://seaborn.pydata.org/

Courses

  • Coursera - Machine Learning by Andrew Ng: https://www.coursera.org/learn/machine-learning
  • Fast.ai - Practical Deep Learning: https://course.fast.ai/
  • Kaggle Learn: https://www.kaggle.com/learn
  • DeepLearning.AI Specializations: https://www.deeplearning.ai/

Tutorials & Guides

  • Scikit-learn Tutorials: https://scikit-learn.org/stable/tutorial/
  • Kaggle Courses: https://www.kaggle.com/learn
  • Towards Data Science: https://towardsdatascience.com/
  • Machine Learning Mastery: https://machinelearningmastery.com/

Datasets

  • UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/
  • Kaggle Datasets: https://www.kaggle.com/datasets
  • OpenML: https://www.openml.org/
  • Google Dataset Search: https://datasetsearch.research.google.com/

Research Papers & Platforms

  • arXiv.org: https://arxiv.org/ (Machine Learning papers)
  • Papers with Code: https://paperswithcode.com/
  • Google Scholar: https://scholar.google.com/

Communities

  • Reddit r/MachineLearning: https://www.reddit.com/r/MachineLearning/
  • Kaggle Forums: https://www.kaggle.com/discussion
  • Stack Overflow: https://stackoverflow.com/questions/tagged/machine-learning
  • Cross Validated: https://stats.stackexchange.com/

Tools & Libraries

Python Libraries

  • NumPy: Numerical computing
  • Pandas: Data manipulation
  • Scikit-learn: Machine learning
  • XGBoost: Gradient boosting
  • Matplotlib/Seaborn: Visualization
  • Imbalanced-learn: Handling imbalanced datasets

Development Tools

  • Jupyter: Interactive notebooks
  • VS Code: Code editor
  • Git: Version control
  • Docker: Containerization

MLOps & Production

  • MLflow: Experiment tracking
  • DVC: Data version control
  • FastAPI: Model serving
  • TensorBoard: Visualization

Key Concepts Index

  • Supervised Learning: Chapters 3, 5, 6
  • Unsupervised Learning: Chapter 7
  • Overfitting: Chapter 4
  • Cross-Validation: Chapters 4, 8
  • Hyperparameter Tuning: Chapter 8
  • Imbalanced Data: Chapter 9
  • Feature Engineering: Chapter 9
  • Pipelines: Chapter 9
  • Gradient Boosting: Chapter 11

Keep this book as a reference as you continue your machine learning journey!