This is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. To officially register for this course you must create a profile in our web app. Just create an account on GitHub and log in with this account in our web app. The intention of this app is to offer for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc.

XGBoost is a popular implementation of Gradient Boosting because of its speed and performance. Internally, XGBoost models represent all problems as a regression predictive modeling problem that only takes numerical values as input. If your data is in a different form, it must be prepared into the expected format. In this post you will discover how to prepare your data for using with gradient boosting with the XGBoost library in Python. Data Preparation for Gradient Boosting with XGBoost in Python Photo by Ed Dunens, some rights reserved.

"Speaker: Ben Hamner This tutorial will offer an introduction machine learning and how to apply it to a Kaggle competition. We will cover methodologies that have worked well across a diverse set of problems, and then work on a current Kaggle competition together using iPython notebook and scikit-learn. We will cover concepts including feature extraction, feature selection, model evaluation, and data visualization.

Data Science is not just about data. The bare basics are recognizing what all data to keep, identifying how to process it for different results. It does not stop there. Data scientists need to figure out blanks in data and fill them with data that'may' come up in future. Data Science essentially is about connecting dots in businesses and using existing and non-existing data to meet the demands of each business.

Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

Even a decade ago, talking to your computer was probably a sign that you'd been working too hard and could do with a lie down. Today, no such stigma applies. That's because chatbots -- the conversational agents capable of simulating intelligent conversations with human users -- have made some massive leaps forward. From changing the way kids learn in schools to picking you out the perfect meal this evening, here are the seven of the most interesting chatbots doing the rounds at the moment. From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn.

This online machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering. In this first chapter, you get your first intro to machine learning. After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail in future chapters. You'll learn how to assess the performance of both supervised and unsupervised learning algorithms.

This video contains the course material of Artificial Intelligence and more lectures are yet to upload on this course please Subscribe if you want to learn about AI. And if you like the video please don't forget to LIKE, COMMENT and SHARE.

There are currently over 100 million students waiting to become the next generation of engineers, rocket scientists and astrophysicists to get humans from Earth to Mars and beyond, but they may not be able to fulfill their potential simply due to a lack of access to a quality education. According to a study from UNESCO, more than 100 million young people worldwide, 62 to 66 million of whom are girls, are not attending school of any kind. Hundreds of millions more are unable to afford good-quality or safe schools. However, groups like OneWeb and ONE are aiming to provide universal Internet access worldwide by 2020, greatly expanding the ability to use educational resources online. Nevertheless, Internet access does not guarantee a quality education.

Have you ever wondered what's the magic behind the tutorials on Large-scale Linear Models and Wide & Deep Learning? I hope this post would at least point you to the right direction. Please take a look at my previous blog posts to understanding some basics of TensorFlow Learn and its integration with other high-level TensorFlow modules. The purpose of this post is to help you better understand the underlying principles of estimators in TensorFlow Learn and point out some tips and hints if you ever want to build your own estimator that's suitable for your particular application. This post will be helpful when you ever wonder how everything works internally and gets overwelmed by the large codebase.