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gradient-boosting

Gradient Boosting: Teaming Up Learners for Superhuman Classification and Regression

h2o-3

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

hands-on-natural-language-processing-with-python

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

human-resource-analytics-and-employee-churn-prediction

A Data science and Analytics project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why and which are the best and most experienced employees leaving prematurely?

hybrid-recommendation-system

Content Based and Collaborative Filtering

ice_breaker

image-caption-generator

LSTM+ RESNET50 for predicitng Captions based on Image. Useful in Youtube tag generator, Caption Generator etc.

interpret-community

The Interpret Community extends Interpret repo with additional interpretability techniques and utility functions to handle real-world datasets and workflows.

k-means-clustering

K-Means Clustering: Unveiling Hidden Groups in Your Data

keras-customer-churn

Customer Churn Shiny Application

langchain

⚡ Building applications with LLMs through composability ⚡

lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

llmspracticalguide

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

llmsurvey

The official GitHub page for the survey paper "A Survey of Large Language Models".

logistic-regression

You will have to build a logistic regression model and interpret the result. Make sure you partition the data set by allocating 70% -for training data and 30% -for validating the results.

lstm

Long Short-Term Memory: The RNN Hero Conquering Long Sequences

machine-learning

Build a model that best explains the employee’s decision to use cars as the main means of transport?

machine-learning-mathematics

machine-learning-with-python

Python code for common Machine Learning Algorithms

machine_learning_examples

A collection of machine learning examples and tutorials.

machlearn

Machine learning dashboard created with R/shiny

market-basket-analysis

The data set provided constitutes the data of a Café Chain for one of its restaurants. We need to do a thorough analysis of the data and come up with the following analysis: •Exploratory Analysis •Menu Analysis •Price Analysis

mitlm

MIT Language Modeling Toolkit

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

naive-bayes

Naive Bayes: A Simple Yet Powerful Classification Algorithm

natural-language-processing

To predict the sentiment for various case studies

ner-annotator

Named Entity Recognition (NER) Annotation tool for SpaCy. Generates Traning Data as a JSON which can be readily used.

ner-training-spacy-3.0

Train your custom NER Pipeline with Spacy in 5 simple steps

nlp-projects-nhv

NLP Projects playlist

nmt

Machine Translation