I found that the best way to discover and get a handle on the basic concepts in machine learning is to review the introduction chapters to machine learning textbooks and to watch the videos from the first model in online courses. Pedro Domingos is a lecturer and professor on machine learning at the University of Washing and author of a new book titled "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World". Domingos has a free course on machine learning online at courser titled appropriately "Machine Learning". The videos for each module can be previewed on Coursera any time. In this post you will discover the basic concepts of machine learning summarized from Week One of Domingos' Machine Learning course.

I have paid 29 for this course (Machine Learning by Andrew Ng) just now and it is written that "May 31st is the last date for purchasing a certificate, after which the course will be offered without a certificate option. As long as you purchase the certificate before May 31st, you can complete the course any time and earn the certificate." However the mailer I have got after enrolling is that I need to complete the course in 180 days. Kindly confirm if there is a 180 day limit or I can complete the course anytime and get a certificate?

University lectures often offer online discussion forums for students to discuss and solve issues with other students and instructors. Correlating the participation of a student in a discussion forum to his performance in the course is subject of current research. Therefore, to qualify the different parts a student plays in a discussion, be it asking or answering a question, is sought in this paper. In current analysis of online discussion forums, such parts are annotated by hand. Thereby, identifying corresponding roles manually is a costly task, which requires the work of more than one person to annotate and approve the chosen roles. The desired step to a better understanding of student online discussion forums is the automated annotation of student roles. A student's role is determined by classifying the student's message into different speech act categories. This paper introduces a supervised speech act classification method for messages in German discussion forums that aims at solving the problem of manually detecting speech acts in online discussion for further discourse analysis. A comparative evaluation shows the significant improvements of the new classifier and its appropriateness for the German language.

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.

Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.

The scikit-learn library is one of the most popular platforms for everyday machine learning and data science. The reason is because it is built upon Python, a fully featured programming language. But how do you get started with machine learning with scikit-learn. Kevin Markham is a data science trainer who created a series of 9 videos that show you exactly how to get started in machine learning with scikit-learn. In this post you will discover this series of videos and exactly what is covered, step-by-step to help you decide if the material will be useful to you.

Machine learning algorithms learn from data. It is critical that you feed them the right data for the problem you want to solve. Even if you have good data, you need to make sure that it is in a useful scale, format and even that meaningful features are included. In this post you will learn how to prepare data for a machine learning algorithm. This is a big topic and you will cover the essentials.

Write on topics related to machine learning Learn from the contributions by others. The app brings 15 subjects, 90 units, 1200 topics on Machine learning, computer science and related courses. The the app includes subjects related to machine learning such as Artificial Intelligence, Algorithms, Mathematics, Automata, Graph theory and more.

Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

In my last post, we saw that AI means a lot of things to a lot of people. These dueling definitions each have a deep history -- ok fine, baggage -- that has massed and layered over time. While they're all legitimate, they share a common weakness: each one can apply perfectly well to a system that is not particularly intelligent. As just one example, the chatbot that was recently touted as having passed the Turing test is certainly an interlocutor (of sorts), but it was widely criticized as not containing any significant intelligence. Let's ask a different question instead: What criteria must any system meet in order to achieve intelligence -- whether an animal, a smart robot, a big-data cruncher, or something else entirely?