100-Days-Of-ML-Code
machine_learning_basics
| 100-Days-Of-ML-Code | machine_learning_basics | |
|---|---|---|
| 4 | 5 | |
| 43,337 | 4,211 | |
| - | - | |
| 0.0 | 0.0 | |
| 4 months ago | 3 months ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
100-Days-Of-ML-Code
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Top 10 GitHub Repositories for Python and Java Developers
5. Avik-Jain/100-Days-Of-ML-Code - As the name implies, this repository offers a structured approach to learning machine learning with Python. It covers core ML principles and algorithms through real-world applications. https://github.com/Avik-Jain/100-Days-Of-ML-Code
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Top 10 GitHub Repositories Every Developer Should Bookmark in 2024
2) 100 Days of ML Code: Embark on a 100-day journey into the fascinating world of machine learning with this structured curriculum. Packed with bite-sized coding challenges and real-world projects, this repository will transform you from a coding novice to a confident ML enthusiast. (https://github.com/Avik-Jain/100-Days-Of-ML-Code)
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✨ 5 Best GitHub Repositories to Learn Machine Learning in 2022 for Free 💯
1️⃣ 100 Days Of ML Code
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The Ultimate Resource Guide for Your Next 100 Days of Code
ML: 100-Days-Of-ML-Code
machine_learning_basics
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Bayesian linear regression in (plain) Python
A while back I open sourced a repository implementing fundamental machine learning algorithms in Python, along with the most important theoretical information. I originally created the repository for myself when preparing for AI residency interviews. You can find the original Reddit post here.
- Bayesian linear regression in Python
What are some alternatives?
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
Data-science-best-resources - Carefully curated resource links for data science in one place
borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.
machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.
mango - Parallel Hyperparameter Tuning in Python
dive-into-machine-learning - Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
trulens - Evaluation and Tracking for LLM Experiments
100DaysOfCode - A GitHub Repo for my #100DaysOfCode challenge projects
rmi - A learned index structure
awesome-python-data-science - Probably the best curated list of data science software in Python.
PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features