ashishpatel26 / Amazing-Feature-EngineeringLinks
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
☆761Updated 6 months ago
Alternatives and similar repositories for Amazing-Feature-Engineering
Users that are interested in Amazing-Feature-Engineering are comparing it to the libraries listed below
Sorting:
- Python Feature Engineering Cookbook, published by Packt☆486Updated last month
- Complete-Life-Cycle-of-a-Data-Science-Project☆633Updated last year
- Code repository for the online course Feature Engineering for Machine Learning☆406Updated 2 years ago
- A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning☆599Updated 7 years ago
- Code repository for the online course Feature Selection for Machine Learning☆338Updated last year
- Data Science Feature Engineering and Selection Tutorials☆290Updated this week
- Code repository for the online course Machine Learning with Imbalanced Data☆184Updated last year
- Interpretable Machine Learning with Python, published by Packt☆477Updated last month
- Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).☆813Updated 5 years ago
- Code repository for the online course Hyperparameter Optimization for Machine Learning☆133Updated last year
- This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJ…☆265Updated 2 years ago
- Applied Machine Learning Explainability Techniques, published by Packt☆246Updated last month
- Labs and Project from the course "How to Win a Data Science Competition: Learn from Top Kagglers"☆149Updated 6 years ago
- Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng☆387Updated 2 years ago
- Cracking the Data Science Interview☆362Updated 6 years ago
- Official Repo for the Efficient Python for Data Scientists Book. You can buy the book from here:☆582Updated 11 months ago
- A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.☆1,628Updated 3 years ago
- Curriculum and roadmap from 0 to Mastery for MLOps. Adding value to your machine learning model by deploying it for people to use it to s…☆184Updated 3 years ago
- A book of subtle code tricks and gem resources for all things data, machine learning and deep learning.☆169Updated last year
- I am sharing my Journey of 66DaysofData in Natural Language Processing.☆190Updated 2 years ago
- Methods with examples for Feature Selection during Pre-processing in Machine Learning.☆363Updated 5 years ago
- How to Win a Data Science Competition: Learn from Top Kagglers☆175Updated 7 years ago
- Source Code for 'Hands-on Time Series Analysis with Python' by B V Vishwas and Ashish Patel☆365Updated 5 years ago
- Code and files to go along with CS329s machine learning model deployment tutorial.☆616Updated 3 years ago
- 🍧 DataCamp data-science and machine learning courses☆407Updated last year
- Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks.…☆211Updated 2 months ago
- Portfolio in Python☆48Updated 2 years ago
- Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt☆217Updated last month
- ☆131Updated 3 years ago
- Practical guidance for time series analysis in Python☆316Updated 5 months ago