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.
☆757Updated 5 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☆485Updated last week
- Code repository for the online course Feature Engineering for Machine Learning☆407Updated 2 years ago
- Complete-Life-Cycle-of-a-Data-Science-Project☆630Updated last year
- A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning☆598Updated 7 years ago
- Code repository for the online course Feature Selection for Machine Learning☆337Updated last year
- Data Science Feature Engineering and Selection Tutorials☆289Updated last week
- Code repository for the online course Machine Learning with Imbalanced Data☆183Updated last year
- Code repository for the online course Hyperparameter Optimization for Machine Learning☆132Updated 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 week
- Interpretable Machine Learning with Python, published by Packt☆477Updated last week
- Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt☆216Updated last week
- Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng☆381Updated 2 years ago
- ☆131Updated 3 years ago
- A book of subtle code tricks and gem resources for all things data, machine learning and deep learning.☆169Updated last year
- Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to …☆187Updated 2 years ago
- Methods with examples for Feature Selection during Pre-processing in Machine Learning.☆363Updated 5 years ago
- 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
- A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.☆1,627Updated 3 years ago
- Cracking the Data Science Interview☆361Updated 6 years ago
- The Ultimate Product Data Science & Analytics Resource☆69Updated 3 years ago
- Probably the best curated list of data science books in Python☆421Updated last month
- Engineering MLOps, published by Packt☆189Updated last week
- 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
- ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xg…☆290Updated 5 years ago
- Code repository for the online course "Feature Engineering for Time Series Forecasting".☆196Updated 2 years ago
- Code for the online course "Deployment of Machine Learning Models"☆877Updated last year
- Machine Learning Experiments and Work☆652Updated 2 years ago
- Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of…☆187Updated 2 years ago
- Practical guidance for time series analysis in Python☆317Updated 4 months ago