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Environmental Overview

1. This content assumes you have a basic understanding of Python programming and familiarity with common libraries such as NumPy and Pandas. For those with less background, I’ve included references to Appendix One: Mathematical Foundations of Machine Learning and Appendix Two: Common Machine Learning Tools.

2. All content is written and executed in Jupyter Notebook for ease of understanding and reproducibility. You can read the Jupyter Notebook Concise Guide in the appendices to learn the basics of using Jupyter Notebook.

3. The chapters on deep learning simplify data volume and network structure, allowing most to run without a GPU, making them more accessible. GPU-specific content will be noted in the respective chapters.

4. All code is tested and runs in Python 3.10, unless otherwise specified. Typically, the code should work with the mainstream versions of related packages. If you encounter errors, try installing the following versions individually; some library installations will also be mentioned in the respective chapters. Due to numerous dependencies and potential version conflicts, a unified requirements.txt file is not provided.

numpy==1.26.1
scipy==1.11.3
pandas==2.1.2
seaborn==0.13.0
matplotlib==3.8.1
scikit-learn==1.3.2
statsmodels==0.11.0
jieba==0.42.1
gensim==4.3.2
hdbscan==0.8.33
graphviz==0.20.1
mlxtend==0.23.0
tensorflow==2.14.0
nltk==3.8.1
flair==0.13.0
onnx==1.15.0

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1. Overview and Examples of Machine Learning