Hey, My name is Nilay Mehta! I am an experienced .Net developer, having the Microsoft certificate of Programming with C#.Net. I have a Master of Computer Applications and Bachelor of Computer Application degrees. I've worked with a range of development tools from PHP, C#, ASP.NET, and ASP.Net core. I am a passionate software engineer who loves learning new technologies, and from the past 3 years, I'm enjoying sharing that knowledge through blogs and courses.
While normal education suffered a standstill in 2020, there were a lot of online courses and programs that were initiated by some of the most prestigious institutions as well as big tech giants so that the process of learning and skill development doesn't suffer. As the trend has been for a few years now, some of the most interesting initiatives were seen in the field of data science. In this article, we have listed some of the prominent data science education programs and initiatives in 2020. Microsoft, in collaboration with Netflix, has launched three new learning modules on beginners concepts in data science, along with machine learning and artificial intelligence. The design of these courses is inspired by the Netflix original film -- 'Over The Moon,' where a young girl Fei Fei, who builds a rocket to the moon, embarks on a mission to prove the existence of Moon Goddess.
TL;DR: The Machine Learning for Beginners Overview Bundle is on sale for £14.80 as of Dec. 20, saving you 96% on list price. Learning this technology is far from a walk in the park, but it's worth it. Ready to give it a shot? Check out this Machine Learning for Beginners Overview Bundle, a three-part pack of classes designed to walk beginners through the basics of machine learning without getting too in the weeds. The content spans seven hours total and requires no prior knowledge.
NASSCOM, at present, is offering several free online courses on artificial intelligence on its newly launched FutureSkills Prime Platform. The digital skilling platform was launched in association with the Ministry of Electronics and Information Technology (MeitY) to drive a national skilling ecosystem for digital technologies such as artificial intelligence, cybersecurity, cloud computing, data analytics, and the Internet of Things among others.
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences. Learn how to perform supervised and reinforcement learning, with images and temporal sequences. This course includes lectures, lecture notes, exercises, labs, and homework problems.
We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource requirements. The mechanism is repeated for multiple rounds and a user's requirements can change on each round. At the end of each round, users provide feedback about the allocation they received, enabling the mechanism to learn user preferences over time. Such situations are common in the shared usage of a compute cluster among many users in an organisation, where all teams may not precisely know the amount of resources needed to execute their jobs. By understating their requirements, users will receive less than they need and consequently not achieve their goals. By overstating them, they may siphon away precious resources that could be useful to others in the organisation. We formalise this task of online learning in fair division via notions of efficiency, fairness, and strategy-proofness applicable to this setting, and study this problem under three types of feedback: when the users' observations are deterministic, when they are stochastic and follow a parametric model, and when they are stochastic and nonparametric. We derive mechanisms inspired by the classical max-min fairness procedure that achieve these requisites, and quantify the extent to which they are achieved via asymptotic rates. We corroborate these insights with an experimental evaluation on synthetic problems and a web-serving task.
We consider the problem of online learning in the presence of sudden distribution shifts as frequently encountered in applications such as autonomous navigation. Distribution shifts require constant performance monitoring and re-training. They may also be hard to detect and can lead to a slow but steady degradation in model performance. To address this problem we propose a new Bayesian meta-algorithm that can both (i) make inferences about subtle distribution shifts based on minimal sequential observations and (ii) accordingly adapt a model in an online fashion. The approach uses beam search over multiple change point hypotheses to perform inference on a hierarchical sequential latent variable modeling framework. Our proposed approach is model-agnostic, applicable to both supervised and unsupervised learning, and yields significant improvements over state-of-the-art Bayesian online learning approaches.
Policy Optimization (PO) is a widely used approach to address continuous control tasks. In this paper, we introduce the notion of mediator feedback that frames PO as an online learning problem over the policy space. The additional available information, compared to the standard bandit feedback, allows reusing samples generated by one policy to estimate the performance of other policies. Based on this observation, we propose an algorithm, RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST), for regret minimization in PO, that employs a randomized exploration strategy, differently from the existing optimistic approaches. When the policy space is finite, we show that under certain circumstances, it is possible to achieve constant regret, while always enjoying logarithmic regret. We also derive problem-dependent regret lower bounds. Then, we extend RANDOMIST to compact policy spaces. Finally, we provide numerical simulations on finite and compact policy spaces, in comparison with PO and bandit baselines.
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