It's 9 am on a February morning and the mercury is just inching past 20 degrees Celsius in Bengaluru. The workday is already two hours old in the metropolis's densely laid-out eastern suburb of Marathahalli. A student batch of both unemployed and working software professionals at Robotek Minds, a tech training institute, has just finished its data science class. Data science is the new buzzword in the tech industry and the code jocks in the Marathahalli class have a singular focus: a job or a leg-up at one of the shiny information technology campuses dotting the city and housing the world's leading tech corporations. Which, they hope, will be a passport to a comfortable salary that will grow in long strides in the years ahead as the use of data in the world economy explodes.

Although the term "Chatbot" is still a buzz word to many people, this revolutionary technology has already been considered "the next big thing" due to massive benefits it can bring to companies across various industries. In this article, we will discuss several chatbot use cases to solidify our claim that businesses should embrace chatbots. According to a research conducted by Chatbots Journal, a leading chatbot community, E-Commerce will benefit the most from chatbots compared to other industries. Successful commerce relies much on B2C (Business-to-Consumer) interaction. Chatbots enrich the relationship between online shops and customers with "conversational commerce", making B2C interaction quicker and friendlier than just a mere business transaction.

On 1st March Google introduced Learn with Google AI Website – a set of educational resources that aims at bringing machine learning skills and makes artificial intelligence accessible to all. Google says the new course has been developed by Machine Learning (ML) experts at the company and is free and available to all users. These educational resources will encourage everyone from beginners to researchers to learn about machine learning concepts by providing a huge range of free, in-depth content, develop skills in the area, and apply AI concept to the real-world problems. Previously, the Machine Learning Crash Course(MLCC) was an internal course at Google, designed to train the Google's employee with a practical introduction to Machine Learning concepts and Artificial Intelligence(AI), as Google continues its moves to be an "AI First" company. Around 18,000 Google employees have already signed up for the course.

This module introduces Machine Learning (ML). We are working on a fix; please see the community page for updates. In the meantime, please try disabling any ad-blocking plugins you have installed in your browser. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 3.0 License, and code samples are licensed under the Apache 2.0 License. For details, see our Site Policies.

Google on Thursday introduced "Learn with Google AI" -- a set of educational resources developed by Machine Learning (ML) experts at the company, for people to learn about concepts, develop skills and apply Artificial Intelligence (AI) to real-world problems. "Learn with Google AI" comes with existing content as well as the new Machine Learning Crash Course (MLCC). "We believe it's important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement. "This is for everyone -- from deep ML experts looking for advanced developer tutorials and materials, to curious people who are ready to try to learn what ML is in the first place," Kemp added.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

JavaScript developers tend to look out for JavaScript frameworks that can be used to train machine learning models based on different machine learning algorithms. In this post, you will learn about different JavaScript framework for machine learning. Deeplearn.js is an open-source machine learning JavaScript library by Google, which can be used for different purposes such as training neural networks in the browser, understanding ML models, for education purposes, etc. You can run pre-trained models in inference mode. One can write the code in Typescript (ES6 JavaScript) or ES5 JavaScript.

GOOGLE HAS announced that it is making its artificial intelligence (AI) and machine learning (ML) courses available to everyone, beyond Mountain View. The new Learn with Google AI portal will let anyone with an interest, at almost any level, learn how to make the most of the glorious new horizon of AI and neural networking. Previously, the courses were designed for internal use to train Googlers, as Google continues its moves to be an "AI First" company. Zuri Kemp from Google AI explains: "From deep learning experts looking for advanced tutorials and materials on TensorFlow, to "curious cats" who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here." There's also a Machine Learning Crash Course (MLCC) which "provides exercises, interactive visualizations, and instructional videos that anyone can use to learn and practice ML concepts."

The hype around artificial intelligence (AI) in medical imaging has led to plenty of discussions of its impact in clinical and academic spaces. To explore current and future implementations of AI in medical imaging at academic institutions, Health Imaging spoke with Curtis Langlotz, PhD, Stanford University's Medical Informatics Director for Radiology. Health Imaging: Where do you think AI will first be deployed in medical imaging? Curtis Langlotz, PhD: Over the next decade, AI will be deployed throughout the image life cycle from image production to image interpretation. For example, machine learning algorithms will produce clearer images using less radiation and will alert technologists to suboptimal images at the scanner console.

The course is a new one by Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain. It will start Aug 15. About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.