https://github.com/mxagar/data_science_python_tools
This repository collects most important ML & DS tools in annotated Jupyter Notebooks.
https://github.com/mxagar/data_science_python_tools
data-science deep-learning machine-learning
Last synced: over 1 year ago
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This repository collects most important ML & DS tools in annotated Jupyter Notebooks.
- Host: GitHub
- URL: https://github.com/mxagar/data_science_python_tools
- Owner: mxagar
- Created: 2021-11-20T14:40:57.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-28T14:34:17.000Z (over 3 years ago)
- Last Synced: 2025-02-15T12:50:46.843Z (over 1 year ago)
- Topics: data-science, deep-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 37.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data Science Tools in Python
This project contains notebooks and notes related to the most important concepts and tools necessary for **machine learning** and **data science**.
I started collecting most of the notebooks and notes while following several web tutorials and Udemy courses, such as:
- [Python for Data Sciene and Machine Learning Bootcamp (by José Marcial Portilla)](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/)
- [Complete Tensorflow 2 and Keras Deep Learning Bootcamp (by José Marcial Portilla)](https://www.udemy.com/course/complete-tensorflow-2-and-keras-deep-learning-bootcamp/)
- [Python for Computer Vision with OpenCV and Deep Learning (by José Marcial Portilla)](https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/)
- [Practical AI with Python and Reinforcement Learning (by José Marcial Portilla)](https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/)
- [Machine Learning A-Z™: Hands-On Python & R In Data Science (by Kirill Eremenko & Hadelin de Ponteves)](https://www.udemy.com/course/machinelearning/)
Unfortunately, sometimes I have not found a repository to fork, so the attribution is done in this `README.md`.
The aforementioned courses are very practical, they don't focus so much on the theory; for that purpose, I used:
- "An Introduction to Statistical Learning with Applications in R", by James et al. A repository with python notebooks can be found in [https://github.com/JWarmenhoven/ISLR-python](https://github.com/JWarmenhoven/ISLR-python).
- "Reinforcement Learning" by Sutton & Barto.
- "Pattern Recognition and Machine Learning" by Bishop. A repository with python notebooks can be found in [https://github.com/ctgk/PRML](https://github.com/ctgk/PRML).
Note that in some cases I also just simply followed the documentation provided in the websites of the used packages.
Important related `howto` files (not public) of mine are (for my personal tracking):
- `~/Dropbox/Learning/PythonLab/python_manual.txt`
- `~/Dropbox/Documentation/howtos/sklearn_scipy_sympy_stat_guide.txt`
- `~/Dropbox/Documentation/howtos/keras_tensorflow_guide.txt`
- `~/Dropbox/Documentation/howtos/pybullet_openai_guide.txt`
- `~/Dropbox/Documentation/howtos/python_reinforcement_learning_openai.txt`
To run the notebooks locally, first, install an environment manager, e.g., [conda](https://docs.conda.io/en/latest/), create an environment and install the required dependencies:
```bash
# Create your env
conda create --name ds pip python=3.8
conda activate ds
# Install all necessary packages
# FIXME: Many packages can be removed
pip install -r requirements.txt
```
Then, you open the notebooks; if I were a beginner, I'd start sequentially.
See also:
- An 80/20 guide for Data Processing: Data Cleaning, Exploratory Data Analysis, Feature Engineering, Feature Selection — [eda_fe_summary](https://github.com/mxagar/eda_fe_summary).
- My notes and the code of the IBM Machine Learning Professional Certificated offered by IBM & Coursera — [machine_learning_ibm](https://github.com/mxagar/machine_learning_ibm).
Mikel Sagardia, 2018.
No guarantees.