atystats
Data Scientist with 6 years of experience in marketing, pricing analytics and assortment.
LbrandsBangalore
atystats's Stars
practical-tutorials/project-based-learning
Curated list of project-based tutorials
fastai/numerical-linear-algebra
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
JWarmenhoven/ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
lisa-lab/DeepLearningTutorials
Deep Learning Tutorial notes and code. See the wiki for more info.
AakashKumarNain/annotated_research_papers
This repo contains annotated research papers that I found really good and useful
markdregan/Bayesian-Modelling-in-Python
A python tutorial on bayesian modeling techniques (PyMC3)
avehtari/BDA_course_Aalto
Bayesian Data Analysis course at Aalto
krasserm/bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
changwookjun/StudyBook
Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
sajal2692/data-science-portfolio
Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
alexattia/Data-Science-Projects
DataScience projects for learning : Kaggle challenges, Object Recognition, Parsing, etc.
dipanjanS/hands-on-transfer-learning-with-python
Deep learning simplified by transferring prior learning using the Python deep learning ecosystem
alexminnaar/time-series-classification-and-clustering
Time series classification and clustering code written in Python.
njtierney/naniar
Tidy data structures, summaries, and visualisations for missing data
dive-into-machine-learning/python-tips
[Archived.] Teammates asked for Python resources; here ya go! :) For more up to date resources go here: https://github.com/alexmojaki/futurecoder and https://github.com/vinta/awesome-python#resources
lucashu1/link-prediction
Representation learning for link prediction within social networks
dgkim5360/the-elements-of-statistical-learning-notebooks
Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman
dlab-berkeley/Machine-Learning-in-R
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
koalaverse/vip
Variable Importance Plots (VIPs)
alexhuang1117/Data-Science-Portfolio
A Portfolio of my Data Science Projects
business-science/presentations
A central repository of Business Science presentations
duttashi/learnr
Exploratory, Inferential and Predictive data analysis. Feel free to show your :heart: by giving a star :star:
WillKoehrsen/data-science-for-good
Data Science for Good Projects
mmarouen/The-Elements-Of-Statistical-Learning
This repository contains R code for exercices and plots in the famous book.
atystats/Time-Series-Forecasting
Notebooks on Time series forecasting
kumarchinnakali/digital-foundry-demand-forcasting
In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.
atystats/Coding-the-matrix-Code
This repository has python codes for "Coding the Matrix" linear algebra in python
AusDTO/dto-digitalmarketplace-analytics
Scripts to extract data from the marketplace, do analysis and produce reports