https://github.com/skalskip/ilearnmachinelearning.py
This repository contains all my small projects related with Data Science and Machine Learning.
https://github.com/skalskip/ilearnmachinelearning.py
Last synced: about 1 month ago
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This repository contains all my small projects related with Data Science and Machine Learning.
- Host: GitHub
- URL: https://github.com/skalskip/ilearnmachinelearning.py
- Owner: SkalskiP
- Created: 2017-09-11T20:20:02.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T00:55:10.000Z (over 2 years ago)
- Last Synced: 2025-04-24T23:46:12.358Z (about 1 month ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 88.9 MB
- Stars: 45
- Watchers: 4
- Forks: 23
- Open Issues: 10
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Metadata Files:
- Readme: README.md
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README
# My Machine Learning
This repository is a collection of my works and projects related to Data Science and Machine Learning. In my scripts I use mainly Python and its dedicated librarys: Pandas, NumPy, SciPy, Sci-Kit Learn, Matplotlib, Basemap Plotly. I had also used some D3 for data visualizations. I also try to make custom implementations of algorithms known from Sci-Kit Learn library.
## Projects
* Regression
* [Simple Linear Regression](https://github.com/SkalskiP/My_Machine_Learning/tree/master/01_Regression/01_Simple_Linear_Regression)
* [Polynomial Regression](https://github.com/SkalskiP/My_Machine_Learning/tree/master/01_Regression/02_Polynomial_Regression)
* Clustering
* [K-Means](https://github.com/SkalskiP/My_Machine_Learning/tree/master/02_Custering/02_K-Means)
* Classification
* [K Nearest Neighbor](https://github.com/SkalskiP/My_Machine_Learning/tree/master/03_Classification/01_K_Nearest_Neighbor)
* [Decision Trees](https://github.com/SkalskiP/My_Machine_Learning/tree/master/03_Classification/02_Decision_Trees)
## Keggle
For some time, I am also quite active on Kaggle:
* [GPUs Dataset Analysis](https://www.kaggle.com/skalskip/using-regression-to-predict-gpus-of-the-future) - using several kinds of regression to predict technical parameters of GPUs in future.
* [FIFA 18 Data Exploration and Visualization](https://www.kaggle.com/skalskip/fifa-18-data-exploration-and-d3-js-visualization) - using D3, plotly and seaborn for data visualization.
* [How big is French Industry?](https://www.kaggle.com/skalskip/how-big-is-french-industry-data-visualization) - deep dive into Kaggle dataset containing information about French industry. The use of libraries such as Plotly and Basemap has allowed me to create truly beautiful visuals.
You can find more of my work related with Machine Learning and data science at my Kaggle Profile.
## Prizes and awards
* **Kaggle Kernels Award Winner** (20.10.2017) for my Kaggle project "Using regression to predict GPUs of the future.
## Sources and articles that interested me
* [What is bias in artificial neural network?](https://www.quora.com/What-is-bias-in-artificial-neural-network)
* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
* [The 5 Clustering Algorithms Data Scientists Need to Know](https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68)
* [8 Tactics to Combat Imbalanced Classes](https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/)
* [Creating a palette for musical scores with machine learning](https://magenta.tensorflow.org/music-vae)
* [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)