Best
17.5K views | +2 today
Follow
Best
Programming

Best

Best or Top of a Category
Curated by Dr. Stefan Gruenwald
Your new post is loading...
Your new post is loading...
Scooped by Dr. Stefan Gruenwald
Scoop.it!

Top iPython Tutorials for Data Science and Machine Learning

Top iPython Tutorials for Data Science and Machine Learning | Best | Scoop.it
From www.datasciencecentral.com - July 9, 2017 8:25 PM
The 11 IPythonTutorials
  • Example Machine Learning - Notebook by Randal S. Olson, supported by Jason H. Moore. University of Pennsylvania Institute for Bioinformatics
  • Python Machine Learning Book - 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action!
  • Learn Data Science - The initial beta release consists of four major topics: Linear Regression, Logistic Regression, Random Forests, K-Means Clustering
  • Machine Learning - This repo contains a collection of IPython notebooks detailing various machine learning algorithms. In general, the mathematics follows that presented by Dr. Andrew Ng's Machine Learning course taught at Stanford University (materials available from ITunes U, Stanford Machine Learning), Dr. Tom Mitchell's course at Carnegie Mellon, and Christopher M. Bishop's "Pattern Recognition And Machine Learning".
  • Research Computing Meetup - Linux and Python for data analysis (tutorials). University of Colorado, Computational Science and Engineering.
  • Theano Tutorial - A brief IPython notebook-based tutorial on basic Theano concepts, including a toy multi-layer perceptron example..
  • IPython Theano Tutorials - A collection of tutorials in ipynb format that illustrate how to do various things in Theano.
  • IPython Notebooks - Demonstrations and use cases for many of the most widely used "data science" Python libraries. Implementations of the exercises presented in Andrew Ng's "Machine Learning" class on Coursera. Implementations of the assignments from Google's Udacity course on deep learning.

 

For more articles about IPython for Data Science and Machine Learning, click here. 

 

DataScience and Machine Learning Resources

 

Additional Reading

more...
No comment yet.
Sign up to comment
Rescooped by Dr. Stefan Gruenwald from Nostri Orbis
Scoop.it!

47 New External Data Science / Machine Learning Resources and Articles

47 New External Data Science / Machine Learning Resources and Articles | Best | Scoop.it
From www.datasciencecentral.com - October 31, 2016 12:58 AM
Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms.

 

  1. Authoring Books with R Markdown 
  2. Feature Selection: The 10-dimensional burrito 
  3. From scatter plot to slope chart 
  4. Using Big Data for Machine Learning Analytics in Manufacturing 
  5. A Complete Tutorial on Linear Regression with R 
  6. Statistical Computing with Stata 
  7. Build an AI Writer - Machine Learning for Hackers - Video
  8. Demystifying linear regression and feature selection 
  9. Monitoring A/B experiments in real-time 
  10. JupyterLab: the next generation of the Jupyter Notebook 
  11. Cheat Sheets for Web Developers 
  12. How to Start Learning Deep Learning 
  13. How to evaluate Data Science models ? 
  14. Variable selection vs Model selection 
  15. Anomaly detection with normal distribution 

 

Other Data Science Resources


Via Fernando Gil
more...
No comment yet.
Sign up to comment
Rescooped by Dr. Stefan Gruenwald from Nostri Orbis
Scoop.it!

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples | Best | Scoop.it
From www.datasciencecentral.com - November 20, 2015 10:58 AM

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization.


The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.


Via Fernando Gil
more...
No comment yet.
Sign up to comment
Rescooped by Dr. Stefan Gruenwald from Data is big
Scoop.it!

Best: Great Github list of public data sets

Best: Great Github list of public data sets | Best | Scoop.it
From www.datasciencecentral.com - April 24, 2015 2:18 PM

Many data set resources have been published on DSC, both big and little data. Some associated with our data science apprenticeship. A list can be found here. Below is a repository published on Github, originally posted here.


Via ukituki
more...
No comment yet.
Sign up to comment
Rescooped by Dr. Stefan Gruenwald from Open Data
Scoop.it!

100+ Interesting Data Sets for Data Science

100+ Interesting Data Sets for Data Science | Best | Scoop.it
From www.datasciencecentral.com - June 27, 2014 6:45 PM

 Looking for interesting data sets? Here's a list of more than 100 of the best stuff, from dolphin relationships to political campaign donations to death row prisoners.


Via Yves Mulkers
more...
No comment yet.
Sign up to comment
Scooped by Dr. Stefan Gruenwald
Scoop.it!

What is Data Science? 24 Fundamental Articles Answering This Question

What is Data Science? 24 Fundamental Articles Answering This Question | Best | Scoop.it
From www.datasciencecentral.com - March 13, 2017 2:00 PM

Many people new to data science might believe that this field is just about R, Python, Hadoop, SQL, and traditional machine learning techniques or statistical modeling. Below you will find fundamental articles that show how modern, broad and deep the field is. Some data scientists are actually doing none of the above.

 

The article on deep data science (see below) shows that data science is also about automating the tasks that many people (calling themselves data scientists) do routinely. And it can be done using very little mathematical / traditional statistical science.

 

Many of these articles should help the beginner to have a better idea about what data science is. Some are technical, but most can be understood by the layman.

 

24 Articles About Core Data Science

more...
No comment yet.
Sign up to comment
Scooped by Dr. Stefan Gruenwald
Scoop.it!

Data Science: Selection of best articles from past weekly digests (2015)

Data Science: Selection of best articles from past weekly digests (2015) | Best | Scoop.it
From www.datasciencecentral.com - February 7, 2016 8:55 PM

The following is a selection of featured articles that were posted in our previous weekly digests, in short, the best of the best on DSC. Single-starred articles are written by external/guest bloggers. Older popular articles are being added regularly, so please check out this page once a week! There is an upcoming book on data science 2.0 (or data science automation or data science handbook or the little data science book, not sure yet about the title) that will be based on some of these (edited and revised) articles.

more...
No comment yet.
Sign up to comment
Scooped by Dr. Stefan Gruenwald
Scoop.it!

20 short tutorials all data scientists should read (and practice)

20 short tutorials all data scientists should read (and practice) | Best | Scoop.it
From www.datasciencecentral.com - January 2, 2015 12:04 PM

Here's the list:

  1. Tutorial: How to detect spurious correlations, and how to find the...
  2. Practical illustration of Map-Reduce (Hadoop-style), on real data
  3. Jackknife logistic and linear regression for clustering and predict...
  4. From the trenches: 360-degrees data science
  5. A synthetic variance designed for Hadoop and big data
  6. Fast Combinatorial Feature Selection with New Definition of Predict...
  7. A little known component that should be part of most data science a...
  8. 11 Features any database, SQL or NoSQL, should have
  9. Clustering idea for very large datasets
  10. Hidden decision trees revisited
  11. Correlation and R-Squared for Big Data
  12. Marrying computer science, statistics and domain expertize
  13. New pattern to predict stock prices, multiplies return by factor 5
  14. What Map Reduce can't do
  15. Excel for Big Data
  16. Fast clustering algorithms for massive datasets
  17. Source code for our Big Data keyword correlation API
  18. The curse of big data
  19. How to detect a pattern? Problem and solution
  20. Interesting Data Science Application: Steganography
more...
No comment yet.
Sign up to comment
About Follow us Resources Terms Mobile Features
Facebook
X
LinkedIn