uea-datascience Goto Github PK

2018-cs109a

Repository for CS109A Fall 2018

arduino-code-optimizer

Reduce memory usage of your Arduino sketch

autspaces

Code respository for AutSPACEs: the Autistica/Turing citizen science platform

blocklyduino-v2

BlocklyDuino v2(020), reboot and up to date with Google Blockly, graphical programming of Arduino boards

c9-python-getting-started

Sample code for Channel 9 Python for Beginners course

cleaning-data

This is my journey of learning how to clean data, following the 5 Day Data Challenge of Cleaning Data https://www.kaggle.com/rtatman/data-cleaning-challenge-handling-missing-values

cleveland-mcgill

Creates charts in the style of Cleveland and McGill's seminal study on graphical perception.

computing-principles

Analysis of Algorithms

crud-demo

Source code for "Building a CRUD app with Node, Express, and MongoDB tutorial"

data-8.github.io

data-analysis-and-machine-learning-projects

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

data-cleaning-101

Data Cleaning Libraries with Python

data-visualization

Data Visualization with Python

data-visualization-with-python

Discover how Matplotlib and Seaborn can help clearly communicate and present your newly acquired insight

data100

back up for data 100, the upper division data science course @ UC Berkeley, taken spring 2018

datascience

A Python library for introductory data science

dive-into-machine-learning

Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

ds1-f20-content

Course materials for the UCSB Data Science Foundations course (DS 1)

ds100-proj2-logisticregression

Data 100 - Project 2: Email Spam Classifier using Logistic Regression

dsc-bias-variance-trade-off

early-ml

A Python step-by-step primer for Machine Learning and Optimization

fastbook

The fastai book, published as Jupyter Notebooks

foundations-of-data-science-with-python

Foundations of Data Science with Python, by John M. Shea, teaches how to begin working with data, create visualizations, conduct statistical tests using resampling, perform analyses and make optimal decisions based on probability theory, and manipulate multi-dimensional data using linear algebra.

hello-world

recap on use of git

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

interactive_machine_learning

IPython widgets, interactive plots, interactive machine learning

introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

jupyter-edu-book

Teaching and Learning with Jupyter

kaggle-data-cleaning-challenge

Learn professional data cleaning techniques! Data cleaning is a key part of data science, but it can be deeply frustrating. Why are some of your text fields garbled? What should you do about those missing values? Why aren’t your dates formatted correctly? How can you quickly clean up inconsistent data entry? In this five day challenge, you'll learn why you've run into these problems and, more importantly, how to fix them! In this challenge we’ll learn how to tackle some of the most common data cleaning problems so you can get to actually analyzing your data faster. We’ll work through five hands-on exercises with real, messy data and answer some of your most commonly-asked data cleaning questions. Here's a day-by-day breakdown of what we'll be learning each day: - Day 1: Handling missing values - Day 2: Data scaling and normalization - Day 3: Cleaning and parsing dates - Day 4: Character encoding errors (no more messed up text fields!) - Day 5: Fixing inconsistent data entry & spelling errors Ready to get started? Just enter your e-mail below to register. How do I know what to do each day? Every day you’ll get an email with instructions for that day’s challenge sent to the address you provide below. What if I need help? You're welcome to ask for help on the forums or in the comments section of the notebook for each day. When is the challenge? This 5-Day Challenge will run from March 26 through March 30 2018. What do I need to know to get started? This challenge will be taught in Python and assumes you have used some Python before. If you haven't, try working through the Kaggle Learn Machine Learning curriculum before you get started to get up to speed.

machine-learning-roadmap

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.