Pinned Repositories
algorithms-in-python
:octocat: Interactive way to learn Algorithms . Feel free to contribute! :mortar_board:
anomalize
Tidy anomaly detection
awesome-actions
A curated list of awesome actions to use on GitHub
beautiful-docs
Pointers to useful, well-written, and otherwise beautiful documentation.
charmingpython
a step by step guide to learn python from scratch
cheatsheets
RStudio Cheat Sheets
correlationfunnel
Speed Up Exploratory Data Analysis (EDA)
daily-coding-problem
Solutions for Daily Coding Problem.
Data-science-best-resources
Carefully curated resource links for data science in one place
Data-Science-Take-Home-Challenges
A list of take home challenges for data science interviews I've compiled
aworleo's Repositories
aworleo/anomalize
Tidy anomaly detection
aworleo/awesome-actions
A curated list of awesome actions to use on GitHub
aworleo/beautiful-docs
Pointers to useful, well-written, and otherwise beautiful documentation.
aworleo/cheatsheets
RStudio Cheat Sheets
aworleo/correlationfunnel
Speed Up Exploratory Data Analysis (EDA)
aworleo/daily-coding-problem
Solutions for Daily Coding Problem.
aworleo/Data-science-best-resources
Carefully curated resource links for data science in one place
aworleo/Data_Science_Using_R
aworleo/deep-blueberry
If you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!
aworleo/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
aworleo/evidently
Interactive reports to analyze machine learning models during validation or production monitoring.
aworleo/fastpages
An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
aworleo/HackerRank
:chart_with_upwards_trend: Efficient solutions and notes for many HackerRank challenges, in several languages
aworleo/Hackerrank_Python_Domain_Solutions
Solutions of challenges of Hackerrank Python domain
aworleo/HackerrankPractice
170+ solutions to Hackerrank.com practice problems using Python 3, С++ and Oracle SQL
aworleo/handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
aworleo/jaqilearning
aworleo/learn-python
📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.
aworleo/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
aworleo/notes
CME211 Notes | Outline ->
aworleo/pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
aworleo/pandas-cookbook
Recipes for using Python's pandas library
aworleo/practice-python
Part of my daily plan for studying Python.
aworleo/programminginpython.com
This repo consists code of all the programs discussed at programminginpython.com website
aworleo/Python
All Algorithms implemented in Python
aworleo/Python-programming-exercises
100+ Python challenging programming exercises
aworleo/r-recipes
R Recipes
aworleo/scientific-python-lectures
Lectures on scientific computing with python, as IPython notebooks.
aworleo/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
aworleo/the-art-of-command-line
Master the command line, in one page