Awesome Machine Learning Tutorial

A curated collection of learning materials covering classical machine learning, data analysis, foundation models, generative models, reinforcement learning, and efficient model training and inference.

The repository currently contains:

• Jupyter Notebook materials for the Machine Learning and Big Data Computing course at Tongji University
• Lecture slides and supplementary materials from ChuangZhi School

This repository is a personal organization of educational materials and is not an official repository maintained by Tongji University, ChuangZhi School, or the original teaching teams.

Repository Structure

Awsome_Machine_Learning_Tutorial/
├── machine_learning/
│   ├── Lecture 01–11: Data collection, Pandas, and data analysis
│   ├── Lecture 12–18: Supervised learning
│   └── Lecture 19–25: Unsupervised learning
├── ChuangZhi_School/
│   ├── Foundation model pre-training and post-training
│   ├── Generative models
│   ├── Reinforcement learning
│   └── Efficient training and inference
└── README.md

1. Machine Learning and Big Data Computing

The machine_learning directory contains teaching materials for the Machine Learning and Big Data Computing course at Tongji University.

The materials are provided as executable Jupyter Notebooks and cover the complete basic workflow of data analysis and machine learning:

• Data collection and web scraping
• Data cleaning and preprocessing
• Data manipulation with Pandas
• Statistical analysis and visualization
• Scikit-learn fundamentals
• Supervised learning
• Unsupervised learning
• Practical applications using real-world datasets

Part I: Introduction and Data Processing

Lecture	Topic	Material
01	Introduction to Machine Learning and Big Data Computing	Notebook
02	Terminology, Data Sources, and Data Collection	Notebook
03	Web Scraping Techniques	[Notebook](machine_learning/第03n讲_网络爬虫技术2025 %281%29.ipynb)
04	Creating Pandas Objects	[Notebook](machine_learning/第04nn讲 Pandas对象的创建.ipynb)
05	Operations on Pandas Series	[Notebook](machine_learning/第05nn讲 Series 对象操作.ipynb)
06	Operations on Pandas DataFrames	[Notebook](machine_learning/第06n讲 DataFrame 对象操作.ipynb)
07	File Operations with Pandas	[Notebook](machine_learning/第07n讲 Pandas文件操作 %281%29.ipynb)
08	Handling Missing Data	[Notebook](machine_learning/第08n讲 处理缺失数据nn.ipynb)
09	Statistical Analysis	[Notebook](machine_learning/第09n讲 统计分析.ipynb)
10	Grouping and Aggregation	[Notebook](machine_learning/第10n讲 分组.ipynb)
11	Data Visualization with Pandas	[Notebook](machine_learning/第11n讲 pandas数据可视化.ipynb)

Part II: Supervised Learning

Lecture	Topic	Material
12	Introduction to Scikit-learn and Machine Learning	[Notebook](machine_learning/第12n讲 初识Scikit-Learn与机器学习.ipynb)
13	Classification and Iris Recognition	[Notebook](machine_learning/第13n讲 有监督学习-分类器与鸢尾花识别n.ipynb)
13A	Gradient Descent: Implementation from Scratch	Supplement
14	Decision Trees and Titanic Survival Prediction	[Notebook](machine_learning/第14n讲 有监督学习-决策树-Titanic乘客生还预测nn.ipynb)
15	Support Vector Machines	[Notebook](machine_learning/第15nn讲 有监督学习-支持向量机SVM-n.ipynb)
15A	KKT Conditions and Lagrange Multipliers	Supplement
16	Multilayer Perceptrons for EEG Signal Recognition	Notebook
17	Text Classification with Naive Bayes	Notebook
18	Linear Regression for Boston Housing Price Prediction	Notebook

Part III: Unsupervised Learning

Lecture	Topic	Material
19	Dimensionality Reduction and High-Dimensional Data Visualization	Notebook
20	Introduction to Cluster Analysis	Notebook
21	K-Means for Simple Image Compression	Notebook
22	Gaussian Mixture Models for Face Synthesis	Notebook
23	DBSCAN for Identifying Densely Populated Urban Areas	Notebook
24	Hierarchical Clustering for Beijing Weather Analysis	[Notebook](machine_learning/第24讲n-无监督学习-Hierarchical Clustering算法-北京天气聚类分析.ipynb)
25	Evaluation Metrics for Clustering	Notebook

2. ChuangZhi School Materials

The ChuangZhi_School directory contains lecture slides on modern artificial intelligence and foundation models.

Topic	Material
Foundation Model Pre-Training	PDF
Foundation Model Post-Training	PDF
Generative Models	[PDF](ChuangZhi_School/SiyuZhu-Generative Models.pdf)
Chapter 3, Parts 1–4	PDF
The Importance of Contrastive Information for Large Language Models	PDF
Foundations of Reinforcement Learning	PDF
Efficient Model Training and Inference	PDF

Getting Started

Clone the Repository

git clone https://github.com/Chen-Shanpu/Awsome_Machine_Learning_Tutorial.git
cd Awsome_Machine_Learning_Tutorial

Run the Jupyter Notebooks

Create a virtual environment:

python -m venv .venv

Activate it on Windows:

.venv\Scripts\activate

Activate it on Linux or macOS:

source .venv/bin/activate

Install common dependencies:

pip install jupyterlab numpy pandas matplotlib seaborn scipy scikit-learn

Some notebooks may require additional packages:

pip install requests beautifulsoup4 lxml openpyxl

Start JupyterLab:

jupyter lab

Then open the notebooks under the machine_learning directory.

Notes

• Most materials are written in Chinese.
• Dependencies may vary between notebooks.
• Some notebooks may reference external datasets, images, videos, or local file paths from the original teaching environment.
• You may need to download the corresponding datasets or modify file paths before running certain cells.
• Some APIs or datasets may have changed or become deprecated in newer library versions.
• The PDF files can be viewed directly on GitHub or downloaded for offline study.

Contributions

Corrections and improvements are welcome. You may open an issue or submit a pull request for:

• Typographical corrections
• Broken links or missing files
• Compatibility fixes for newer Python library versions
• Improved explanations
• Additional environment instructions
• Better organization of the materials

Please do not upload private course information, copyrighted third-party datasets, or materials that are not permitted for public distribution.

Acknowledgements

The original materials were prepared by their respective instructors, lecturers, and contributors.

This repository was organized and uploaded by Chen-Shanpu to make the materials easier to browse, study, and reproduce.

Disclaimer

This repository is an independently organized educational collection. It is not an official repository maintained by Tongji University, ChuangZhi School, or any original course provider.

All copyrights and intellectual property rights associated with the original materials remain with their respective authors and copyright holders. The materials are shared for educational and non-commercial purposes only.

If any material requires correction, additional attribution, or removal, please open an issue in this repository.