This repository provides a comprehensive, structured learning roadmap for artificial intelligence, targeting beginners and aspiring professionals. It offers nearly 200 practical case studies and projects, along with free accompanying educational materials, to guide users from zero knowledge to job-ready skills in areas like machine learning, deep learning, computer vision, and natural language processing.

How It Works

The project is structured as a learning path, starting with foundational Python and mathematics, then progressing through machine learning algorithms, data analysis, and deep learning frameworks (TensorFlow, PyTorch, Keras, Caffe). It emphasizes hands-on practice with real datasets and provides code implementations, often from scratch, to foster a deep understanding of underlying mechanisms. The approach prioritizes practical application and iterative learning, mirroring real-world development workflows.

Quick Start & Requirements

• Installation: Primarily uses Anaconda for environment management.
• Prerequisites: Python, Anaconda, Jupyter Notebook. Specific projects may require libraries like NumPy, Pandas, Matplotlib, Seaborn, OpenCV, TensorFlow, PyTorch, Keras, and Scikit-learn.
• Resources: Access to datasets, often provided via Baidu Netdisk or Google Drive links. Some datasets can be large.
• Documentation: The README itself serves as the primary guide, with links to external video courses and the project's PDF textbook.

Highlighted Details

• Offers nearly 200 practical AI case studies and projects, developed over five years of teaching.
• Includes a free PDF version of the textbook "跟着迪哥学Python数据分析与机器学习实战" (Learn Python Data Analysis and Machine Learning with Brother Di).
• Provides code implementations for classic algorithms from scratch, emphasizing understanding over direct library usage.
• Covers a wide range of AI domains, including Computer Vision (OpenCV, Mask R-CNN) and Natural Language Processing (BERT).

Maintenance & Community

The project is maintained by 唐宇迪 (Tang Yudi), who also authored a published textbook. Community engagement is encouraged via GitHub stars and direct contact for course-related resources.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. However, the free distribution of the textbook PDF and the emphasis on learning suggest a permissive educational intent. Compatibility for commercial use would require clarification of the license.

Limitations & Caveats

Dataset links are primarily hosted on Baidu Netdisk, which may have accessibility issues outside of China. Some advanced topics and projects are presented as optional or for those with more time and energy. The project's focus is heavily on learning and practice, with less emphasis on production-readiness or deployment strategies.