This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics. The content aims to strike a good balance between mathematical notations, educational implementation from scratch using Python's scientific stack including numpy, numba, scipy, pandas, matplotlib, pyspark etc. and open-source library usage such as scikit-learn, fasttext, huggingface, onnx, xgboost, lightgbm, pytorch, keras, tensorflow, gensim, h2o, ortools, ray tune etc. Notes related to advertising domain. Information Retrieval, some examples are demonstrated using ElasticSearch. End to end project including data preprocessing, model building. Includes: Quick review of necessary statistic concepts.

In a previous post, I described how I was captivated by the virtual landscape imagined by the RStudio education team while looking for resources on the RStudio website. In this post, I'll take a look at Cheatsheets another amazing resource hiding in plain sight. Apparently, some time ago when I wasn't paying much attention, cheat sheets evolved from the home made study notes of students with highly refined visual cognitive skills, but a relatively poor grasp of algebra or history or whatever to an essential software learning tool. I don't know how this happened in general, but master cheat sheet artist Garrett Grolemund has passed along some of the lore of the cheat sheet at RStudio. One day I put two and two together and realized that our Winston Chang, who I had known for a couple of years, was the same "W Chang" that made the LaTex cheatsheet that I'd used throughout grad school.

This section contains case study questions that concern designing machine learning systems to solve practical problems. This section contains portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Movie Recommendation: Designed 4 different models that recommend items on the MovieLens dataset. Trip Optimizer: Used XGBoost and evolutionary algorithms to optimize the travel time for taxi vehicles in New York City. Instacart Market Basket Analysis: Tackled the Instacart Market Basket Analysis challenge to predict which products will be in a user's next order.

In the global tech market, cutting-edge technologies like artificial intelligence, neural net, machine learning, and data analytics are flourishing. ML professionals need cheatsheets to get a deeper understanding of the details. It is not easy to grasp these technologies in a short time. Advanced mechanisms make datasets and machinery concepts more complicated. ML cheatsheets, data analysis cheatsheets, and neuron cheatsheets are all necessary to be successful in this highly competitive market.

Below is an extract of a 10-page cheat sheet about data science, compiled by Maverick Lin. This cheatsheet is currently a reference in data science that covers basic concepts in probability, statistics, statistical learning, machine learning, deep learning, big data frameworks and SQL. The cheatsheet is loosely based off of The Data Science Design Manual by Steven S. Skiena and An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Inspired by William Chen's The Only Probability Cheatsheet You'll Ever Need, located here. Full cheat sheet available here as a PDF document.

Wouldn't it be handy to have a cheat-sheet for a search engine you use daily? That too for a search engine that has tons of goodness to make life easy and display instant results?! Yes, we too thought the same and here is a smart sheet that can actually help you to get better and desired search results easily and quickly. If you are a regular user of DuckDuckGo, scroll to get the cheat-sheet, for others, here is a quick briefing about the search engine. DuckDuckGo is a search engine just like Google, Yahoo, Bing and so on! But, there is a great difference and the difference is what we treasure – privacy!

This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 230 Deep Learning course, and include: This material is also available on a dedicated website, so that you can enjoy reading it from any device. Would you like to see these cheatsheets in your native language? You can help us translating them on this dedicated repo!

VIP Cheat Sheets - Deep Learning by Stanford's CS 229 Students Download whole PDF of Supervised Learning Cheatsheet: From Here VIP Cheat Sheets - Machine Learning Tips by Stanford's CS 229 Students Download whole PDF of Supervised Learning Cheatsheet: From Here VIP Refresher: Probabilities and Statistics Cheatsheet Download whole PDF of Probability and Statistics Cheatsheets: From Here VIP Refresher: Linear Algebra and Calculus Cheat Sheets Download whole PDF of Linear Algebra and Calculus Cheatsheets: From Here You may like this: 100 Free Machine Learning Books Super VIP Cheat Sheet: Machine Learning Download whole PDF of Super VIP Machine Learning Cheat Sheet: From Here

This page contains cheat sheets for various Machine Learning related topics that can come in handy either during ML/DS interviews, or your daily data-scientist life. The page is updated continuously for more cheatsheets.

This is a list that I compiled over the years, it contains everything I found to be useful or interesting. There is no special categorization, it flows a bit to JS side but there little bit of everything. Please feel free to comment and add your favorite repos. Rough.js is a small ( 9 kB) graphics library that lets you draw in a sketchy, hand-drawn-like, style. The library defines primitives to draw lines, curves, arcs, polygons, circles, and ellipses.