For extensive instructor led learning
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Updated
Oct 31, 2022 - Jupyter Notebook
For extensive instructor led learning
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Machine Learning in R
Code repository for the online course Feature Selection for Machine Learning
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Feature engineering package with sklearn like functionality
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.
This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"
Features selector based on the self selected-algorithm, loss function and validation method
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Data Science Feature Engineering and Selection Tutorials
Feature Selection using Genetic Algorithm (DEAP Framework)
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
EvalML is an AutoML library written in python.
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
Leave One Feature Out Importance
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
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