Jan2019 Feb Tutorials, Overviews
All (69) | Courses, Education (4) | Meetings (13) | News (10) | Opinions (20) | Top Stories, Tweets (6) | Tutorials, Overviews (13) | Webcasts & Webinars (3)
- Running R and Python in Jupyter
- Feb 19, 2019.
The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R
- Are BERT Features InterBERTible?
- Feb 19, 2019.
This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?
- How to Setup a Python Environment for Machine Learning
- Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
- A comprehensive survey on graph neural networks
- Feb 15, 2019.
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
- Decision Trees — An Intuitive Introduction
- Feb 14, 2019.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.
-
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning
- Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library. - A Quick Guide to Feature Engineering
- Feb 11, 2019.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
- Understanding Gradient Boosting Machines
- Feb 6, 2019.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.
- 6 Data Visualization Disasters – How to Avoid Them
- Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
- From Good to Great Data Science, Part 1: Correlations and Confidence
- Feb 5, 2019.
With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.
-
Intuitive Visualization of Outlier Detection Methods
- Feb 5, 2019.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own. - Using Caret in R to Classify Term Deposit Subscriptions for a Bank
- Feb 4, 2019.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this.
- Trending Deep Learning Github Repositories
- Feb 1, 2019.
Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.