Learning Management



Python and R -- Unequivocal Champions of Data Science

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This article will discuss about basic programming languages that you need for doing data science. For essential math skills needed, please see the following: Essential Math Skills for Machine Learning.


Free Online Course: Fundamentals of Machine Learning from Complexity Explorer Class Central

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Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works. In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning.


Finland is challenging the entire world to understand AI by offering a completely free online course - initiative got 1 % of the Finnish population to study the basics University of Helsinki

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Finnish technology firm Reaktor and the University of Helsinki joined forces to educate people on AI for free. The institutions combined to develop an online course to teach the basics of AI to anyone interested in the technology. Reaktor and the University also challenged organizations to train their staff in AI, so far over 200 organisations have pledged to do so – including banks, telecoms, and healthcare organizations. Almost 90 000 students have signed up for the course since it began in May. While popular with Finns, the course is already seeing strong demand globally, attracting students from over 80 different countries.


Python is one of the most popular languages inside India

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Python has massive applications in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company according to Coursera. The top 10 list of courses, such as "Programming for Everybody," Python Data Structures," Python for Data Science and AI," has been dominated by python. Python has a lot of advantages. One of them is that it is extremely easy getting started with. It offers a lot of flexibility.


Applied Data Science with Python Coursera

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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.


Python most popular programming language In India

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New Delhi: When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like'Programming for Everybody', 'Python Data Structures', 'Python for Data Science and AI' and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the repor said.


Python most popular programming language in India - OrissaPOST

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New Delhi: When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like'Programming for Everybody', 'Python Data Structures', 'Python for Data Science and AI' and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open-source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the report said.


Online Learning in The Manifold of Low-Rank Matrices

Neural Information Processing Systems

When learning models that are represented in matrix forms, enforcing a low-rank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of low-rank matrices are either prohibitively time consuming (repeated singular value decomposition of the matrix) or numerically unstable (optimizing a factored representation of the low rank matrix). We build on recent advances in optimization over manifolds, and describe an iterative online learning procedure, consisting of a gradient step, followed by a second-order retraction back to the manifold. While the ideal retraction is hard to compute, and so is the projection operator that approximates it, we describe another second-order retraction that can be computed efficiently, with run time and memory complexity of O((n m)k) for a rank-k matrix of dimension m x n, given rank one gradients. We use this algorithm, LORETA, to learn a matrix-form similarity measure over pairs of documents represented as high dimensional vectors.


Online Learning of Assignments

Neural Information Processing Systems

Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize value of information? These applications exhibit strong diminishing returns: Selection of redundant ads and information sources decreases their marginal utility. We show that these and other problems can be formalized as repeatedly selecting an assignment of items to positions to maximize a sequence of monotone submodular functions that arrive one by one. We present an efficient algorithm for this general problem and analyze it in the no-regret model.