With the increasing requirement for more data scientists, ML experts, and AI engineers in every industry, Microsoft, in partnership with Netflix, has launched three new learning modules to guide learners through beginning concepts in data science, machine learning and artificial intelligence. Inspired by the new Netflix original film -- 'Over the Moon' these learning modules include three paths -- planning a Moon mission using the Python Pandas Library; predicting meteor showers using Python and VC Code; and using AI to recognise objects in images using Azure Custom Vision. The growing requirement of data scientists comes with criteria of having a broad set of skills from data analysis with no-code and low-code solutions which will help them with designing and writing intricate ML models and solve some of the planet's most difficult problems. Keeping this in mind, Microsoft, partnering with Netflix, has launched these modules for providing high quality, free content to help learners develop their skills depending based on their professional goals and personal interests. According to Microsoft's blog post, "One such endeavour in creating an opportunity for you to learn and upskill is through unique partnerships. In the summer of 2020, we launched a set of Microsoft Learn modules inspired by real NASA scientists and engineers at https://aka.ms/LearnInSpace. And this Fall we are excited to bring you three more Microsoft Learn modules inspired by the new Netflix Original Over the Moon."

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Participants who register for the course and make valid submissions for all assignments will be eligible to receive a Certificate of Completion by Jovian. Selected projects will also be receive a Best Project Award based on evaluation criteria determined by the instructors. Aakash is the co-founder and CEO of Jovian, a project management and collaboration platform for machine learning. Prior to starting Jovian, Aakash worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from Indian Institute of Technology, Bombay.

The Indian Institute of Science (IISc), Bangalore is organizing an Online Workshop on AI & IoT for Flow Modeling on November 20, 2020, from 11 am to 2 pm. The Indian Institute of Science (IISc) is a public, deemed, research university for higher education and research in science, engineering, design, and management. It is located in Bangalore (Bengaluru), in the Indian state of Karnataka. The workshop is organized as part of the Indo-Dutch project, "Digital Twins for pipeline transport networks". The aim of the project is to develop a digital twin that connects sensor data and advanced fluid solvers in order to detect possible leakage of fluid from the pipeline in real-time.

Free Coupon Discount Preview this course Udemy -  Artificial Intelligence A-Z™: Learn How To Build An AI, Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, SuperDataScience Support

Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, excited, and robust projects to better understand each concept under each topic.

Preview this course - GET COUPON CODE Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Here is the details about the project. Here we will star from colab understating because that will help to use free GPU provided by google to train up our model. We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.

Researchers from Imperial College London and the Swiss Federal Laboratories for Materials Science ... [ ] and Technology have proposed the combined discipline of "Physical AI" as a means for developing lifelike autonomous robots. Researchers at Imperial College London have proposed "physical artificial intelligence" as a new multidisciplinary area of research that could be vital to producing lifelike intelligent robots in the future. Writing in the Nature Machine Intelligence journal, the team argue that teaching materials science, mechanical engineering, computer science, biology and chemistry as a combined discipline would help students and researchers develop lifelike artificially intelligent robots. This combined discipline of "physical AI" could effectively be the missing link in the attempt to create artificially intelligent robots that look and behave like humans, the Imperial College London team suggests. They argue that research into how to build lifelike robot bodies has failed to keep up with advances in computational artificial intelligence, and that the study and practice of physical artificial intelligence could rectify this imbalance.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc.This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.