This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here. One of the pivotal moments in my professional development this year came when I discovered Coursera.
Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns.
The data needed to train machine learning systems comes in a form that computers don't immediately understand. To translate the things we understand naturally (e.g.
Data science projects offer you a promising way to kick-start your analytics career. Not only you get to learn data science by applying, you also get projects to showcase on your CV. Nowadays, recruiters evaluate a candidate’s potential by his/her work, not as much by certificates and resumes.
To understand visual patterns within the dataset quickly and efficiently, we worked with artist Kyle McDonald to overlay thousands of drawings from around the world. This helped us create composite images and identify trends in each nation, as well across all nations.
Alarmed that decades of crucial climate measurements could vanish under a hostile Trump administration, scientists have begun a feverish attempt to copy reams of government data onto independent servers in hopes of safeguarding it from any political interference.
What do 50 million drawings look like? Over 15 million players have contributed millions of drawings playing Quick, Draw! These doodles are a unique data set that can help developers train new neural networks, help researchers see patterns in how people around the world draw, and help artists cr
This post has been translated into Chinese here. I sometimes receive emails asking for guidance related to data science, which I answer here as a data science advice column. If you have a data science related quandary, email me at email@example.com.
By Dataiku.com Sponsored Post. We hear the term “machine learning” a lot these days, usually in the context of predictive analysis and artificial intelligence. Machine learning is, more or less, a way for computers to learn things without being specifically programmed.
There are many data analysis tools available to the python analyst and it can be challenging to know which ones to use in a particular situation. A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets.
In the not-too-distant past, this kind of human-computer interaction would have blown away technologists and delighted consumers — but in 2016, it’s nothing special. Conversations with Siri are commonplace, just like they are with Microsoft’s Cortana and Amazon’s Alexa.
Data is ubiquitous — but sometimes it can be hard to see the forest for the trees, as it were. Many companies of various sizes believe they have to collect their own data to see benefits from big data analytics, but it’s simply not true.
For years, we’ve used percentage of passes completed as an evaluation tool for how good a passer a player is. The problem is that basic passing percentages are meaningless for player evaluation.
Does your data have a purpose? If not, you’re spinning your wheels. Here’s how to discover one and then translate it into action.
Gradient descent is one of those “greatest hits” algorithms that can offer a new perspective for solving problems. Unfortunately, it’s rarely taught in undergraduate computer science programs.
Facebook puts an extremely demanding workload on its data backend. Every time any one of over a billion active users visits Facebook through a desktop browser or on a mobile device, they are presented with hundreds of pieces of information from the social graph.
Machine Learning has many advantages. It is the hot topic right now. For a trader or a fund manager, the pertinent question is “How can I apply this new tool to generate more alpha?”. I will explore one such model that answers this question in a series of blogs.
Machine learning is easily one of the biggest buzzwords in tech right now. Over the past three years Google searches for “machine learning” have increased by over 350%.
How can I become a data scientist? originally appeared on Quora – the knowledge sharing network where compelling questions are answered by people with unique insights.
Big data! If you don’t have it, you better get yourself some. Your competition has it, after all. Bottom line: If your data is little, your rivals are going to kick sand in your face and steal your girlfriend.
You’re walking home alone on a quiet street. You hear footsteps approaching quickly from behind. It’s nighttime. Your senses scramble to help your brain figure out what to do. You listen for signs of threat or glance backward.
Why is data visualization so important in statistics, anyway? Graphs and other kinds of visualizations might seem superfluous, if you’re using statistical analysis to look for patterns in a data set, right? Short answer: wrong.
The full code is available on Github. In this post we will implement a model similar to Kim Yoon’s Convolutional Neural Networks for Sentence Classification.
Frequently Asked Questions about R How can I subset a data set? The R program (as a text file) for all the code on this page. Subsetting is a very important component of data management and there are several ways that one can subset data in R.
After following the fantastic R tutorial “Titanic: Getting Stated with R”, by Trevor Stephens on the Kaggle.com Titanic challenge, I felt confident to strike out on my own and apply my new knowledge on another Kaggle challenge.
In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable.
Artificial intelligence is getting its teeth into lip reading. A project by Google’s DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programmes to create a lip-reading system that leaves professionals in the dust.
Tinder users have many motives for uploading their likeness to the dating app. But contributing a facial biometric to a downloadable data set for training convolutional neural networks probably wasn’t top of their list when they signed up to swipe.
When working with data, a key part of your workflow is finding and importing data sets. Being able to quickly locate data, understand it and combine it with other sources can be difficult. One tool to help with this is data.world, where you can search for, copy, analyze, and download data sets.
Andrew Beam does a great job showing that small datasets are not off limits for current neural net methods. If you use the regularisation methods at hand – ANNs is entirely possible to use instead of classic methods. Let’s see how this holds up on up on some benchmark datasets.
A few days ago on Hacker News, I saw a nice submission titled “Statistics for Hackers,” which was a slide deck written and presented by Jake Vanderplas.
Functors and monads are powerful design patterns used in Haskell. They give us two cool tricks for analyzing data. First, we can “preprocess” data after we’ve already trained a model. The model will be automatically updated to reflect the changes.
It’s been a while since I’ve written an article on Data Science for Losers. A big Sorry to my readers. But I don’t think that many people are reading this blog. Now let’s continue our journey with the next step: Machine Learning.
Careful! These question can make you think THRICE! Machine learning and data science are being looked as the drivers of the next industrial revolution happening in the world today. This also means that there are numerous exciting startups looking for data scientists.
There are no shortcuts for data exploration. If you are in a state of mind, that machine learning can sail you away from every data storm, trust me, it won’t. After some point of time, you’ll realize that you are struggling at improving model’s accuracy.
How to If you’ve heard the excitement about machine learning, but aren’t quite sure how it could apply to your business, the best way forward is to rip off the cover and see it working for yourself.
Normally distributed data is a commonly misunderstood concept in Six Sigma. Some people believe that all data collected and used for analysis must be distributed normally. But normal distribution does not happen as often as people think, and it is not a main objective.
We love data, big and small and we are always on the lookout for interesting datasets. Over the last two years, the BigML team has compiled a long list of sources of data that anyone can use.
At Facebook, you don’t have to be a “data scientist” to tackle tough data problems. That’s what we heard from Justin Moore, whose data science career spans a pair of financial firms as well as Foursquare and Facebook.
There’s this fallacy among many content marketers and SEOs. Data is one of the best ways to create kick-ass content and build links. But my business/client doesn’t have any interesting data to share.
If you were to stumble upon the whole microservices thing, without any prior context, you’d be forgiven for thinking it a little strange. Taking an application and splitting it into fragments, separated by a network, inevitably means injecting the complex failure modes of a distributed system.
If you have spent some time in machine learning and data science, you would have definitely come across imbalanced class distribution. This is a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other classes.
Apache Spark and Spark MLLib for building price movement prediction model from order log data. This post is based on Modeling high-frequency limit order book dynamics with support vector machines paper.
What does “causality” mean, and how can you represent it mathematically? How can you encode causal assumptions, and what bearing do they have on data analysis? These types of questions are at the core of the practice of data science, but deep knowledge about them is surprisingly uncommon.
You’ve got a presentation due in a few days and you really want to impress the boss. You want to show your data in a way that’s easy to understand, but also visual and impressive. The only way to do that is to choose the right chart for your data.
Python is fast becoming the preferred language for data scientists – and for good reasons. It provides the larger ecosystem of a programming language and the depth of good scientific computation libraries. If you are starting to learn Python, have a look at learning path on Python.