Mar
2016 Apr Tutorials, Overviews
All (108) | Courses, Education (8) | Meetings (13) | News, Features (28) | Opinions, Interviews, Reports (29) | Software (5) | Tutorials, Overviews (19) | Webcasts (6)
- Dealing with Unbalanced Classes, SVMs, Random Forests, and Decision Trees in Python
- Apr 29, 2016.
An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.
- How to Remove Duplicates in Large Datasets
- Apr 27, 2016.
Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is de-duplication, find out how you can solve this using the statistical techniques.
- Deep Learning in Neural Networks: An Overview
- Apr 26, 2016.
This post summarizes Schmidhuber's now-classic (and still relevant) 35 page summary of 900 deep learning papers, giving an overview of the state of deep learning as of 2014. A great introduction to a great paper!
- Top 10 IPython Notebook Tutorials for Data Science and Machine Learning
- Apr 22, 2016.
A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.
- Holding Your Hand Like a Small Child Through a Neural Network – Part 2
- Apr 21, 2016.
The second of 2 posts expanding upon a now-classic neural network blog post and demonstration, guiding the reader through the workings of a simple neural network.
- Holding Your Hand Like a Small Child Through a Neural Network – Part 1
- Apr 20, 2016.
The first part of this 2 part series expands upon a now-classic neural network blog post and demonstration, guiding the reader through the foundational building blocks of a simple neural network.
- Comprehensive Guide to Learning Python for Data Analysis and Data Science
- Apr 20, 2016.
Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.
- Deep Learning for Chatbots, Part 1 – Introduction
- Apr 19, 2016.
The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.
- Uplift Modeling Opportunities at PAW Chicago, June 20-23, 2016, and PAW New York
- Apr 19, 2016.
At Chicago's Predictive Analytics World for Business conference, June 20-23, 2016, and at New York’s PAW Business conference, uplift modeling will be covered in eight ways: across keynotes, sessions, and an article by PAW founder Eric Siegel. KDnuggets subscribers enjoy $150 off!
- The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line
- Apr 18, 2016.
A great guide for the MBA, or any relatively non-technical convert, for getting comfortable with the command line and other technical skills required to excel in data science.
- Association Rules and the Apriori Algorithm: A Tutorial
- Apr 14, 2016.
A great and clearly-presented tutorial on the concepts of association rules and the Apriori algorithm, and their roles in market basket analysis.
- How to Grow Your Own Data Scientists
- Apr 14, 2016.
How Zynga is “home growing” its own data science talent from the inside, by retraining some of our top analysts and engineers to become data scientists.
- Regression & Correlation for Military Promotion: A Tutorial
- Apr 13, 2016.
A clear and well-written tutorial covering the concepts of regression and correlation, focusing on military commander promotion as a use case.
- From Science to Data Science, a Comprehensive Guide for Transition
- Apr 12, 2016.
An in-depth, multifaceted, and all-around very helpful roadmap for making the switch from 'science' to 'data science,' yet generally useful for data science beginners or anyone looking to get into data science.
- A Pocket Guide to Data Science
- Apr 11, 2016.
A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.
- Deep Learning from 30,000 feet
- Apr 9, 2016.
My very-high level overview of Deep Learning for Delta Sky Magazine, including neurons, a conspiracy, games, amazing feats of superhuman ability, and more - appropriate for reading at 30,000 feet.
- Tricking Deep Learning
- Apr 8, 2016.
Deep neural networks have had remarkable success with many tasks including image recognition. Read this overview regarding deep learning trickery, and why you should be cognizant.
- Basics of GPU Computing for Data Scientists
- Apr 7, 2016.
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
- Deep Learning for Internet of Things Using H2O
- Apr 6, 2016.
H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.
Learn How to Listen: One of the hardest parts of being a data scientist
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning