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.

This course covers Amazon's API Gateway service, which will help you create some incredibly robust APIs for your workloads.

Start today and improve your skills. So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 2.6 million students to learn about the future today! Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python! This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning.

This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.

A complete guide to begin your python learning for data science, data analysis and machine learning. For those, who has never written a single code in entire life and want to move into data science or advanced python, this course provides you a simple approach to learn coding from scratch using python as a tool and master it with illustrations and assignments. For those, who are already experienced in coding, but want to move into advanced python, this course provides you ample hands-on exercises and assignments for deeply understanding the concept. In this course, you will be learning from the very basics - which includes basic numbers, arithmetic operations, lists, sets, tuples, dictionaries, loops, if else statements, nested dictionaries, functions, recursive functions etc. We will be using Jupyter notebook in order to execute all the codes.

In this course the student will learn how to use Google Colab and Python's machine learning library, sklearn, to create datasets and use ... In this course the student will learn how to use Google Colab and Python's machine learning library, sklearn, to create datasets and use them in machine learning enterprises. The datasets will be created in sklearn and they are comprised of classifications and regressions, being twenty in total. When the datasets have been created, machine learning techniques will be employed to make predictions on the labels. In addition, the concepts of supervised and unsupervised learning will be discussed. Although most of the examples will be of supervised learning, clustering will be brushed upon.

Every Sunday, NDL holds a lecture that covers Machine Learning topics starting from Linear Regression up to Deep Learning, and is taught by the President. This course covers the fundamentals of ML and Artificial Intelligence with hands-on experience of Python libraries. Come to NDL Fireside Chats where you are able to listen and speak to alumni from IBM, Microsoft, Amazon, and many more. It is a great opportunuty to learn from the best in their fields and become even become friends! Firesides happen both in person and in Zoom, so everyone has a chance to attend.