By now, you might already know machine learning, a branch in computer science that studies the design of algorithms that can learn. Today, you're going to focus on deep learning, a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. These algorithms are usually called Artificial Neural Networks (ANN). Deep learning is one of the hottest fields in data science with many case studies with marvelous results in robotics, image recognition and Artificial Intelligence (AI). One of the most powerful and easy-to-use Python libraries for developing and evaluating deep learning models is Keras; It wraps the efficient numerical computation libraries Theano and TensorFlow.

Our fourth installment of Research for Practice covers two of the hottest topics in computer science research and practice: cryptocurrencies and deep learning. First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open access Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. This is a topic with a long history in the academic literature that has recently come to prominence with the rise of Bitcoin, blockchains, and similar implementations of advanced, decentralized protocols. These developments--and colorful exploits such as the DAO vulnerability in June 2016--have captured the public imagination and the eye of the popular press. In the meantime, academics have been busy, delivering new results in maintaining anonymity, ensuring usability, detecting errors, and reasoning about decentralized markets, all through the lens of these modern cryptocurrency systems.

In the fall of 2016, the citizens of the United States became fed up and made a decision that was heard around the world. While not all states felt the same way about this decision, it was pretty clear that it was time for some real change. The last 8 years had been an utter disappointment, full of the some of the most mindless inane drivel the world has ever seen, making people truly question if the United States was really the great nation that it thought it was. Then, last fall, it finally happened. On or around October 9th, the citizens of the United States of America collectively became more interested in artificial intelligence than Kim Kardashian's net worth.

Machine learning features in X-Pack let you automate the task of detecting anomalies in time series data. In the first video in this tutorial series, we show you how to detect anomalies in single univariate time series via a single metric job type. Download the example from GitHub to try this out on your machine.

For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below. For this task, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

At universities, "people learn how to approach new things, ask questions and find answers, deal with new situations," wrote Uta Russmann, a professor of communications at the FHWien University of Applied Sciences in Vienna. "All this is needed to adjust to ongoing changes in work life. Special skills for a particular job will be learned on the job." Schools will also need to teach traits that machines can't yet easily replicate, like creativity, critical thinking, emotional intelligence, adaptability and collaboration. The problem, many respondents said, is that these are not necessarily easy to teach.

This course shows you how to accomplish some common NLP (natural language processing) tasks using Python, an easy to understand, general programming language, in conjunction with the Python NLP libraries, NLTK, spaCy, gensim, and scikit-learn. The course is designed for basic level programmers with or without Python experience. Jonathan Mugan is CEO and co-founder of DeepGrammar, a natural language processing company. Jonathan has a PhD in computer science from the University of Texas, and has been working in AI and machine learning since 2003. He describes his research focus as "making the squishy reality of our everyday world available to computation."

I'm a software engineer with 10 years of experience who recently decided to switch my focus to machine learning. I did the coursera course and did CS231n: Convolutional Neural Networks for Visual Recognition, read up on basic theory, did some image processing networks like VGG, Resnets and most recently trying to get Faster-RCNN to work, so my currently knowledge is ML basics and heavily focussed on ML in the Image domain. I recently landed my first ML job at a company that does mostly NLP, so I lack a lot of knowledge in that domain. I'm currently reading the NLTK book, which has been very approachable in introducing basic concepts in a code-focussed way. So I was wondering if anyone could point me to some good mid to advanced level resources (online courses/videos/books) to get up to speed with where the field is at now, to help me understand current research and more advanced concepts?

Accelebrate's Python data science training course teaches data scientists, statisticians, and other quantitative professionals the Python skills they need to use Python to analyze and chart data. Please contact us for a quote and to see how we can customize a private, onsite Python class for your team.

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).