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alifrmf
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<div align="center"> <a href="https://github.com/denvercoder1/readme-typing-svg"> <img width="80%" src="https://readme-typing-svg.herokuapp.com?font=Orbitron&size=25&color=cb3365&background=1A1B27¢er=true&vCenter=true&duration=3000&pause=300&lines=<Hey,+There!+๐>;<This+is+Ali!>;<Glad+to+meet+you!>"> </a>  <div style="display: flex; justify-content: space-between; align-items: center;"> <img src="https://github-readme-stats.vercel.app/api?username=alifrmf&show_icons=true&theme=radical" width="42%" alt="GitHub stats"> <img src="https://github-readme-stats.vercel.app/api/top-langs/?username=alifrmf&show_icons=true&theme=radical" width="32%" alt="Top Langs"> </div> --- ## <span style="font-family: Arial;">๐ ๏ธSkills๐ ๏ธ</span> ### <span style="font-family: Arial;">Languages:</span>   ### <span style="font-family: Arial;">Libraries & Frameworks:</span>   [](https://matplotlib.org/) [](https://seaborn.pydata.org/)       ### <span style="font-family: Arial;">Tools:</span>    ### <span style="font-family: Arial;">Other:</span>  [](https://colab.research.google.com/) [](https://www.anaconda.com/) [](https://jupyter.org/) ## <h3 align="center">Follow Me On:</h3> <p align='center'> <a href="https://www.kaggle.com/alifarahmandfar"><img height="30" src="https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge&logo=Kaggle&logoColor=white"></a> <a href="https://medium.com/@ali.farahmandfar"><img height="30" src="https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=Medium&logoColor=white"></a> </p> ## <h3 align="center">Contact Me:</h3> [](mailto:https://ali.farahmand101@gmail.com) [](https://t.me/alifrm5) --- <h3 align="center">โ๏ธHere's a preview of my latest Medium articles:</h3>  ## > **๐A Comprehensive Analysis of Hyperparameter Optimization in Logistic Regression Models๐** > > In this article, we will follow a systematic approach to fine-tune the logistic regression algorithmโs hyperparameters. Our process will consist of the following steps: 1. Manually adjusting each hyperparameter, 2. Evaluating the impact of each hyperparameter on the modelโs accuracy, 3. Identifying optimal values for the hyperparameters. Once we have determined the best hyperparameters, we will compare the performance of various models using these optimal values. Finally, we will provide a comprehensive analysis of the effectiveness of hyperparameter tuning in enhancing the modelโs performance. > > Read the full article on [Medium](https://levelup.gitconnected.com/a-comprehensive-analysis-of-hyperparameter-optimization-in-logistic-regression-models-521564c1bfc0). ## > **๐What a Data Scientist Should Know about Machine Learning Kernels?๐** > > This article provides an overview of the concept of kernels in machine learning. It explains what kernels are and their purpose in transforming input data into a higher-dimensional space where patterns are easier to identify and classify. The article describes different types of kernels including linear, polynomial, Gaussian, sigmoid, Laplacian, cosine similarity, and histogram intersection kernels. It also discusses the limitations of kernels and the importance of hyperparameter tuning in kernel-based algorithms. > > Read the full article on [Medium](https://medium.com/mlearning-ai/what-a-data-scientist-should-know-about-machine-learning-kernels-4066f02d1f26).
