Data science plays an important role in virtually all aspects of business operations and strategies. For example, it provides information about customers that helps companies create stronger marketing campaigns and targeted advertising to increase product sales. It aids in managing financial risks, detecting fraudulent transactions, and preventing equipment breakdowns in manufacturing plants and other industrial settings. It helps block cyber-attacks and other security threats in IT systems. We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies.

We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

This is an Amazing course of 20 lectures with practical implementation along with codes and notes for each and every lecture. You will be learning this course step by step and will be interacting with all the topics along will practical of each and every topic considering all the conditions . This course is enough to set all your logics and concepts on place and in description you will get all the codes .

We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly parametrized deep assignment flows with random initial conditions. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables (i) to use risk bounds as training objectives for learning posterior distributions on the hypothesis space and (ii) to compute tight out-of-sample risk certificates of randomized classifiers more efficiently than related work. Comparison with empirical test set errors illustrates the performance and practicality of this self-certifying classification method.

Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.

This series explores the accelerating digital strategies leaders are using to expand, integrate, and empower distribution channels to deliver greater value and better experiences to insureds while driving growth, efficiencies, and profits for insurers and their partners. Machine learning & AI are being used by digital leaders to help them increase distribution effectiveness by improving the insured experience, and to optimize channel management & processes. Join thought leaders as they discuss the strategies, results and lessons learned by those on the leading edge. One of InsurTech's top influencers, author, speaker and consultant in connected insurance, innovation, transformation and leadership. Insurance expert with 30 Yrs Sr. leadership experience in marketing, underwriting, claims, ops., reinsurance, analytics, & technology.

Move your ML skills from theory to practice in one of the most interesting fields " Physics"? In this course you are going to solve the glass identification problem where you are going to build and train several machine learning models in order to classify 7 types of glass( 1- Building windows float-processed glass / 2- Building windows non-float-processed glass / 3- Vehicle windows float-processed glass / 4- Vehicle windows non-float-processed-glass / 5- Containers glass / 6- Tableware glass / 7- Headlamps glass). After completing this course, you will gain a bunch of skillset that allows you to deal with any machine learning problem from the very first step to getting a fully trained performent model.

Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems? It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch.

Shlomi Hod (shlomi@bu.edu) is a computer science Ph.D. student at Boston University, USA. Karni Chagal-Feferkorn (karni111@gmail.com) is a Postdoctoral Fellow in AI and Regulation at the Faculty of Law, Common Law Section, University of Ottawa, Canada. Niva Elkin-Koren (elkiniva@tauex.tau.ac.il) is a Professor of Law at Tel Aviv University, Faculty of Law, Israel. Avigdor Gal (avigal@ie.technion.ac.il) is the Benjamin and Florence Free Chaired Professor of Data Science at Technion--Israel Institute of Technology, Israel.