10-steps-to-become-a-data-scientist

📢 Ready to learn!✅ you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.🔖

20-ml-algorithms-from-start-to-finish-for-iris

I want to solve Iris problem (Hello World) a popular machine learning Dataset as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template to deal with machine learning problems.

2020

Materials for Applied Data Analysis CS-401, fall 2020

500lines

500 Lines or Less

98

💿 Web-based Windows 98 desktop recreation █████▓█▓▓▒▓▒▒░▒░░░🗕︎🗗︎🗙︎

a-comprehensive-deep-learning-workflow-with-python

This tutorial demonstrates the basic workflow for Deep Learning.You should be familiar with basic linear algebra,Python and the Jupyter Notebook editor. It also helps if you have a basic understanding of Machine Learning and classification.

academicons

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

adeshpande3.github.io

Build a Jekyll blog in minutes, without touching the command line.

advanced-density-peaks

aes-illustrated

An implementation of the Advanced Encryption Standard (AES) algorithm meant for study to go along with "A Stick Figure Guide to the Advanced Encryption Standard (AES)" blog post at www.moserware.com

ai-reading-materials

Some of the ML and DL related reading materials, research papers that I've read

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

alishakiba

applied-deep-learning

Applied Deep Learning Course

approachingalmost

Approaching (Almost) Any Machine Learning Problem

arx

ARX is a comprehensive open source data anonymization tool aiming to provide scalability and usability. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.

asciiclass

Notes and Labs for Advanced Topics in Data Processing

autograder_samples

Examples of autograders for running on Gradescope

awesome-courses

:books: List of awesome university courses for learning Computer Science!

awesome-graph-embedding

A collection of important graph embedding, classification and representation learning papers with implementations.

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. Propose NEW data ☛☛☛PR☛☛☛

backend

Job Guy Backend

basic-toolbox-sample-code

Sample Code for “Sequential and Parallel Algorithms and Data Structures -- The Basic Toolbox” Book

basics-datamining-workshop

bayesian-modelling-in-python

A python tutorial on bayesian modeling techniques (PyMC3)

beamer-progressbar

berkeley-stat-157

Homepage for STAT 157 at UC Berkeley

big-data-fall-2022

bigdata-2018f-lintool

CS 451/651 Data-Intensive Distribute Computing (Fall 2018) at the University of Waterloo