Ds Student Resources
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Keras | 58,529 |  | 330 | a day ago | 68 | May 13, 2022 | 389 | apache-2.0 | Python | |
| Deep Learning for humans | ||||||||||
| Scikit Learn | 54,487 |  | 18,944 | 6,722 | a day ago | 64 | May 19, 2022 | 2,191 | bsd-3-clause | Python |
| scikit-learn: machine learning in Python | ||||||||||
| Ml For Beginners | 48,894 | 2 days ago | 13 | mit | Jupyter Notebook | |||||
| 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all | ||||||||||
| Made With Ml | 33,193 | a month ago | 5 | May 15, 2019 | 11 | mit | Jupyter Notebook | |||
| Learn how to responsibly develop, deploy and maintain production machine learning applications. | ||||||||||
| Spacy | 26,286 |  | 1,533 | 842 | 2 days ago | 196 | April 05, 2022 | 107 | mit | Python |
| 💫 Industrial-strength Natural Language Processing (NLP) in Python | ||||||||||
| Ray | 25,916 |  | 80 | 199 | a day ago | 76 | June 09, 2022 | 2,905 | apache-2.0 | Python |
| Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. | ||||||||||
| Streamlit | 25,120 |  | 17 | 404 | a day ago | 182 | July 27, 2022 | 633 | apache-2.0 | Python |
| Streamlit — A faster way to build and share data apps. | ||||||||||
| Data Science Ipython Notebooks | 25,025 | a month ago | 33 | other | Python | |||||
| Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||
| Applied Ml | 24,242 | 13 days ago | 3 | mit | ||||||
| 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. | ||||||||||
| Ai Expert Roadmap | 24,033 | 3 months ago | 13 | mit | JavaScript | |||||
| Roadmap to becoming an Artificial Intelligence Expert in 2022 | ||||||||||
DS-Student-Resources
Data Science Student Companion Notebook Series
Curriculum
Curriculum Datasets
About this Repo
The GitHub repository woz-u/DS-Student-Resources contains a collection of Jupyter notebooks and data sets that are used by students in the Data Science program at Woz U. The notebooks cover a variety of topics, including machine learning, data visualization, and natural language processing. The data sets are used to train and evaluate the models that are developed in the notebooks.
The repository is organized into the following folders:
- notebooks: This folder contains the Jupyter notebooks.
- data: This folder contains the data sets.
- images: This folder contains images that are used in the notebooks.
- references: This folder contains links to additional resources.
The notebooks are written in Python and use the following libraries:
- numpy: This library provides a high-level interface to numerical arrays.
- pandas: This library provides data structures and analysis tools for structured (tabular, multidimensional, potentially heterogeneous) and time series data.
- matplotlib: This library provides a comprehensive library for creating static, animated, and interactive visualizations in Python.
- seaborn: This library provides a high-level interface for creating attractive and informative statistical graphics in Python.
- scikit-learn: This library provides a comprehensive collection of machine learning algorithms.
- and many others to include R and R packages
The data sets are in a variety of formats, including CSV, JSON, and text. The data sets are used to train and evaluate the models that are developed in the notebooks.
The repository is a valuable resource for students who are learning Data Science. The notebooks provide a hands-on introduction to the concepts and tools of Data Science. The data sets can be used to train and evaluate models. The repository is a great way to get started with Data Science.
