Learning Path : Your mentor to become a machine learning expert
Not only there is a plethora of resources available, they also age very fast. Couple this with a lot of technical jargon and you can see why people get lost while pursuing machine learning. However, this is only part of the story. You can not master machine learning with out undergoing the grind yourself. You have to spend hours understanding the nuances of feature engineering, its importance and the impact it can have on your models. Through this learning path, we hope to provide you an answer to this problem. We have deliberately loaded this learning path with a lot of practical projects. You can not master machine learning with the hard work! But once you do, you are one of the highly sought after people around. Since this is a complex topic, we recommend you to strictly follow the steps in sequential order. Consider this as your mentor for machine learning. Only skip a step, if you know the subject matter mentioned in that step already.

To the point: 7 reasons you should use dot graphs
Points go beyond where lines and bars stop. Sounds weird, especially for those who remember from their math classes that a line is an infinite collection of points. But in visualization, points can do so much more then lines. Here are seven reasons why you should use more dot graphs, with some examples.

Deep Dreams (with Caffe)
This notebook demonstrates how to use Caffe neural network framework to produce ‘dream’ visuals shown in the Google Research blog post.

Algoriths to search for the best gesture keyboard by Peter Norvig
Typing quickly and accurately on a smartphone screen is hard! One invention to make it easier is gesture typing, in which your finger can trace a path consisting of letter-to-letter segments. When you lift your finger the path (and the word) is complete. Below we see the path for the word ‘hello.’ Note that the path is imprecise; it didn’t quite hit the ‘L’, but the word was recognized anyways, because ‘Hello’ is a known word, whereas ‘Hekko’, ‘Hwerklo’, etc., are not.

Too Big Data: Coping with Overplotting
Scatter plots are a wonderful way of showing (apparent) relationships in bivariate data. Patterns and clusters that you wouldn’t see in a huge block of data in a table can become instantly visible on a page or screen. With all the hype around Big Data in recent years it’s easy to assume that having more data is always an advantage. But as we add more and more data points to a scatter plot we can start to lose these patterns and clusters. This problem, a result of overplotting, is demonstrated in the animation below.

3-step lesson, going into the life of machine learning
I want to develop a model that automatically learns over time’, a really challenging objective. We’ll develop in this post a procedure that loads data, build a model, make predictions and, if something changes over time, it will create a new model, all with R.

Chart makeover – Unisys Security Insights Survey
It seems that not a day goes by without some information security vendor releasing a report based on a survey. Thankfully, this post is not about the efficacy of survey-based reports or their findings. Today, we’re doing a makeover for Unisys, who just released their Findings from the 2015 Unisys Security Insights Survey.