On Thursday 26 November Professor Andrew Murray joined us online to deliver his lecture'Almost human: law and human agency in the time of artificial intelligence.' More than five hundred people from all over the world watched the sixth Annual T.M.C. Asser Lecture. A recording of the lecture is now available on our Youtube page. Artificial intelligence (AI) is all around us: from the smartphones in our hands to drone strikes thousands of miles away. While this technology has many benefits such as simplifying complex data and making daily tasks easier, it also has dangerous implications.

Robotic Process Automation or RPA is a game-changing technology that streamlines and automates repetitive tasks, and thereby minimizing errors to zero level and increasing productivity to a new level. The goal of this course is to help you see the big picture of RPA and prepare you for building software robots using Automation Anywhere, the global leader in Robotic Process Automation and the pioneers of the Digital Workforce.

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

I have always worked with data, since high school, a long time ago in a galaxy far far away. Specifically, my background is astrophysics, with a Ph.D. in the subject. I performed astronomical data analysis, modeling, and simulation for 25 years, while also working on data repositories for space science satellite missions at NASA. I became very interested in the scientific discovery opportunities of very large datasets in the late 1990's, at which time I began my quest into machine learning, data mining, and data science. The motivation for me has always been discovery, from my early days until now.

In our previous article, we saw some of the free courses in data science and AI that were launched this year. With this article, we are listing down some of the degree and diploma programmes that were launched by Indian institutes, colleges and universities along with analytics and data science training institutes. The course list includes all the graduate, postgraduate, diploma and certificate programmes that were launched in domains such as AI, data science, analytics, cybersecurity, blockchain and other new-age technologies. These courses are not free and have programme fee associated, which in most cases have been mentioned here. The list is in no particular order.

As Gen Z enters the workforce, like each generation before them, they are poised to disrupt routines, cultures, and even what it means to do business. A disruptor in its own right, COVID-19 has accelerated the transition to a remote work model, and workers across the spectrum are demanding a flexible and better-balanced future. Among all this upheaval, perhaps the most critical change coming to corporations is the opportunity to revamp learning systems to engage workers and lift the reputation of compliance and other "mandatory" training. A study conducted by Barnes and Noble College found that more than half of younger learners learn best by doing, instead of simply listening. Workers of all ages want to be involved in their own educational pathway.

Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mappings that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we propose a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of patterns present therein.

This tutorial covers the entire ML process, from data ingestion, pre-processing, model training, hyper-parameter fitting, predicting and storing the model for later use. Let's see the whole picture Recreating the entire experiment without PyCaret requires more than 100 lines of code in most libraries. The library also allows you to do more advanced things, such as advanced pre-processing, ensembling, generalized stacking, and other techniques that allow you to fully customize the ML pipeline and are a must for any data scientist. PyCaret is an open source, low-level library for ML with Python that allows you to go from preparing your data to deploying your model in minutes. Allows scientists and data analysts to perform iterative data science experiments from start to finish efficiently and allows them to reach conclusions faster because much less time is spent on programming. When working on a data science project, it usually takes a long time to understand the data (EDA and feature engineering). So, what if we could cut the time we spend on the modeling part of the project in half?

Amazon has revealed it is expanding its range of free online STEM (science, technology, engineering and mathematics) activities to keep children educate and entertained over the Christmas holidays. The tech company's selection of activities includes a new game called Cyber Robotics Challenge. This three-hour long event tasks a youngster with using maths to ensure a friend's birthday present gets delivered by an Amazon fulfilment centre robot. Amazon has also expanded its popular educational platform Maths4All to include secondary school-level activities as well as those geared towards younger pupils. Maths4All offers hundreds of worksheets on Kindle and Fire Tablets and maths challenges via Alexa.

Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding