Andrew Ng [Co-Founder of Coursera, Stanford Professor, Chief Scientist at Baidu, and All-Around Machine Learning Expert] is writing a book during the summer of 2016. The book is titled, Machine Learning Yearning. It you visit the site and signup quickly you can get draft copies of the chapters as they become available. Andrew is an excellent teacher. His MOOCs are wildly successful, and I expect his book to be excellent as well.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.

During the company's Worldwide Partner Conference, Microsoft has unveiled its upcoming plans to provide online degrees that cater to the demands of highly competitive technological fields. Officially launched as the Microsoft Professional Degree (MPD) program, the first course offered to interested professionals and fresh graduates alike, mainly focuses on skill development and education through a Data Sciences curriculum. "Recognizing a shortage of qualified individuals to fill the growing need for data scientists, Microsoft consulted with education and industry partners to develop a curriculum concentrated on developing the skills and real world experience these new roles require," says Microsoft. This specific MPD program features courses that educate incoming applicants on how they can visualize and implement data in Microsoft Excel and Power BI, as well as supplemental (and needed) skills in R and Python programming language, statistics and machine learning. "At Microsoft, we believe the approach and tools used for learning need to continually evolve to meet the demands of our device-centric and data-driven world," said Alison Cunard, the general manager at Microsoft Learning Experiences.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it. This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how machine learning can be applied. It also includes an increasing number of machine learning enthusiasts with atypical backgrounds who are joining the conversation, bringing in diverse experiences and points of view. The increasingly symbiotic relationship between companies that need machine learning expertise, and data science competition platforms like Kaggle, has greatly impacted how rapid advancement is being achieved. This relationship has also changed the hiring landscape.

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.

Learn how to build a personal chatbot for Facebook Messenger. I have created this step by step guide so you can create your own Facebook Messenger bot without coding. Facebook Messenger has a growing audience of 900 mln. It is an awesome opportunity to showcase your work and promote your services, automate conversations and build out your personal brand. By the end of the course you will be launch and promote your personal bot.

In June on /r/MachineLearning, there were free videos, free books, free courseware, and a quality curriculum made up of free offerings. The word of the month for June is clearly a four letter word starting with'F'. This lot of videos covers a wide range of topics, from general AI, to design issues, to cloud computing, to a variety of machine learning topics and beyond. Microsoft Research has added heavily to these offerings on what seems to be a daily basis since this Reddit post as well. Free knowledge from a top research institute in the field is always welcome.

CRF, HMM, MEMM - that I can do for sequence tagging, never tried it for something like this, it's probably extra hard. Although, I find probabilistic graphical models lacking and have redirected my efforts towards Learning to Search methods. If you check Structured models for fine-to-coarse sentiment analysis by McDonald et al. (2007), you'll see their structured prediction model is a CRF that is a bit hierarchical. You can use Leon Bottou's sgdcrf and adapt the model to get their model (little changes in the forward-backward and viterbi). The complexity of learning and inference for a single example for sentiment of document, paragraphs and sentences is O(M · (M2 P PM2 T)) O(M3 P T), where M is number of possible categorical values, P is number of paragraphs and T the average number of sentences in the paragraph. That's slow as fuck, although still fast if sgdcrf is used (about 500-2000 sentences per second).