This tutorial introduces machine learning and data science concepts for developers. On the web, we already have many excellent resources for learning data science, however, the sheer amount of material can, in itself, be daunting. This is based on my insights from the Enterprise AI course and also the Data Science for IoT course which I teach at Oxford University. We explain concepts simply but in context. Many tutorials explain one specific aspect but do not show how it fits into the wider picture.

"How to Become a Super Learning Machine" is an excellent course that focuses on the practical basics of how to learn. The course teaches students about the right attitude to take when learning, the best way to absorb knowledge and how to set goals and achieve them. Thanks to my experience as a teacher, I went into the course understanding most of the concepts that Joe Parys covers. However, thanks to Joe's progressive and hybrid attitude towards learning I was able to take away some new things that have already helped me in my studies. First, Joe covers the importance of surrounding yourself with positive influence.

Register below to listen to and/or download Ardent's latest webinar recording: Ardent Partners' analysts, Andrew Bartolini and Matt York lead a highly interactive discussion on the Procurement Technology Outlook for 2017. Building on Ardent's extensive repository of sourcing and procurement automation research, data, and expertise, this webinar will provide attendees with a "technology and innovation outlook" for 2017 that will help them understand the impact of today's progressive solutions on all relevant processes including strategic sourcing, procurement/P2P, and supplier management. This webinar will also focus on the technologies that unlock greater strategic value in the areas of Big Data and analytics as well as artificial intelligence, machine learning, sourcing optimization, and other cutting-edge technologies. This webinar will utilize Ardent Partners' latest market research data and the expertise of its analyst team to help Chief Procurement Officers and other procurement and sourcing leaders understand the latest technology trends and innovations and ultimately identify the best-fit solutions for their requirements and budgets. This webinar will be a must-attend for any procurement or supply management leader looking to make a near-term technology investment.

Last week, we learned about Random Forest Algorithm. Now we know it helps us reduce a model's variance by building models on resampled data and thereby increases its generalization capability.

For many years I have been part of discussions about how to diversify computing, particularly about how we recruit and retain a more diverse cohort of computer science (CS) students. I wholeheartedly support this goal, and spend a considerable amount of my effort as chair of ACM-W helping to drive programs that focus on one aspect of this diversification, namely encouraging women students to stay in computing. Of late I have become very concerned about how some elements of the diversity argument are being expressed and then implemented in teaching practices. Problem 1. Women are motivated by social relevance, so when we teach them we have to discuss ways in which computing can contribute to the social good. Problem 2. Students from underrepresented minorities (URM) respond to culturally relevant examples, so when we teach them we have to incorporate these examples into course content.

Techno-optimism is defined as the belief that technology can improve the lives of people. It was famously satired in the U.S. television comedy series "Silicon Valley," with a startup-company's founders pledging to "make the world a better place through Paxos algorithms for consensus protocols." But some people take techno-optimism very seriously. Ray Kurzweil, an accomplished tech innovator, described his techno-optimistic vision in his books: The Age of Spiritual Machines, How to Create a Mind: The Secret of Human Thought Revealed, and The Singularity Is Near. In a keynote address (see https://goo.gl/RwkwK1) at the 2016 meeting of the Computing Research Association, Kentaro Toyama argued that "In spite of the do-gooder rhetoric of Silicon Valley, it is no secret that computing technology in and of itself cannot solve systemic social problems."

You will learn the main data structure in R, Managing Data with R, Exploring and Understanding Data in R (exploring numeric variable, categorical variable and relationship between variables). Moreover, audience learn how to use some of the main packages in R. You will learn different types of machine learning algorithms and how they work and how they can solve different type of real life problems. At the end of this module you will able to choose right algorithm for right problem.

Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don't have to be symmetric with the weights used for propagation the activation forward. In fact, random feedback weights work evenly well, because the network learns how to make the feedback useful. In this work, the feedback alignment principle is used for training hidden layers more independently from the rest of the network, and from a zero initial condition. The error is propagated through fixed random feedback connections directly from the output layer to each hidden layer. This simple method is able to achieve zero training error even in convolutional networks and very deep networks, completely without error back-propagation. The method is a step towards biologically plausible machine learning because the error signal is almost local, and no symmetric or reciprocal weights are required. Experiments show that the test performance on MNIST and CIFAR is almost as good as those obtained with back-propagation for fully connected networks. If combined with dropout, the method achieves 1.45% error on the permutation invariant MNIST task.

Bayesian Reasoning and Machine Learning by David Barber has a chapter on Approximate Sampling Christophe Andrieu et al. have written an introductory tutorial (pdf) on MCMC methods that covers most of the MCMC algorithms Dr. Daphne Koller offers an online course on Coursera, Probabilistic Graphical Models, which also covers the Gibbs Sampler and the Metropolis-Hastings Algorithm Dr. A. Taylan Cemgil has prepared very useful lecture notes (pdf) for his Monte Carlo methods course

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.