--Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. T o reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i.d.-distributed data within a datacenter . In this paper, we consider adaptive degree of sparsity and non-i.i.d. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation tradeoff that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to 40% improvement in model accuracy for a finite training time. Modern consumer and enterprise users generate a large amount of data at the network edge, such as sensor measurements from Internet of Things (IoT) devices, images captured by cameras, transaction records of different branches of a company, etc. Such data may not be shareable with a central cloud, due to data privacy regulations and communication bandwidth limitation [1]. In these scenarios, federated learning (FL) is a useful approach for training machine learning models from local data [1]-[5]. The basic process of FL includes local gradient computation at clients and model weight (parameter) aggregation through a server. Instead of sharing the raw data, only model weights or gradients need to be shared between the clients and the server in the FL process.

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.

Abstract--In this paper, we examine the fundamental performance limitations of online machine learning, by viewing th e online learning problem as a prediction problem with causal side information. T owards this end, we combine the entropic analysis from information theory and the innovations appro ach from prediction theory to derive generic lower bounds on the prediction errors as well as the conditions (in terms of, e.g., d irected information) to achieve the bounds. It is seen in general tha t no specific restrictions have to be imposed on the learning algo rithms or the distributions of the data points for the performance b ounds to be valid. In addition, the cases of supervised learning, s emi-supervised learning, as well as unsupervised learning can a ll be analyzed accordingly. We also investigate the implication s of the results in analyzing the fundamental limits of generalizat ion.

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.

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.

Telemarketing advertising campaigns are a billion-dollar effort and one of the central uses of the machine learning model. However, its data and methods are usually kept under lock and key. The Project is related to the direct marketing campaigns of a banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

You will also gain a greater understanding of the working relationship between humans and AI and what skills are predicted to be needed to work and interact with the technology-- As well as the new jobs that artificial intelligence has and will create. With this understanding, you will be able to hone your own skills to adapt your career to thrive within the future workplace. Technology now exists that can monitor natural disasters and provide warning signals earlier and more accurately to help reduce the number of casualties, or help the emergency services quickly locate victims. Technology now exists that allows people to purchase items simply by taking a picture. These are all examples of artificial intelligence.

Are you looking for the Best IT Training Online? Grab this Best IT Courses Online which will help you to get the Best IT Certifications Online to skyrocket your career. Information Technology Certifications will assist you to understand the real-life implementation of Artificial Intelligence (AI), Data Analytics and Cloud Computing how this has changed the way we work and the way we think. Taking these IT Certifications Online 2020 will assist you to gain robust knowledge in IT sector and new doors will open for you too. Revolutionary changes have taken places in the IT sector due to some big companies like Space X, Amazon, eBay, Microsoft, Facebook and so on.

From social media to speech recognition, warfare to writing articles, coding to customer service – machine learning and artificial intelligence have become part and parcel of our urban lives. In the pursuit to make life smoother and more comfortable, man has tapped into the potential of AI and invented auto-driven cars, smart sensors to capture spectacular photos, and home assistant devices. Similarly, AI is taking the field of education by storm and replacing traditional methods by the minute. Thanks to AI, the academic world has become more personalized, thus changing the way of e-learning. People can now access educational materials with just a click on their phones and laptop.

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.