Artificial intelligence (AI) is increasingly a part of how businesses stay competitive – they're using it to predict customer behaviour, automate tasks, personalise customer experiences, manage supply chains and more. And AI-powered technology isn't just for large enterprises – many small businesses are also enjoying the benefits of AI. It comes as no surprise that nearly three-quarters of small and medium-sized businesses (SMBs) will invest in software and apps over the next year, according to a 2018 Vistage survey. But would you have guessed that – along with CRM – AI tops the list of technologies that SMBs are planning to invest in? At Dreamforce 2018, Vistage's Chief Research Officer, Joe Galvin, shared the whats, hows and whys of AI for small and medium-sized businesses in the session'Customer Insights: How AI Is Revolutionising SMBs Today'.

Gujarat Techological University (GTU) in association with IIT Kanpur organised a two day workshop on artificial intelligence, deep learning, block chain and machine learning. The workshop was attended by students and professors from both Gujarat and Rajasthan. This course will be available both online and offline said GTU, VC, Navin Sheth during the workshop. "To keep the pace with rapidly changing technologies, Gujarat Technological University (GTU) is planning to launch Diploma and Certificate courses on Emerging Technologies like Artificial Intelligence, Machine Learning, and Block Chain etc. both offline & online. More Skill Development initiatives will be launched for making them more employable. More Faculty Development Programs are being planned for providing training about the latest aspects."

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR), and summarises the status quo of applying artificial intelligence (AI) techniques. We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum. Then, we transform the relationship model into the perception-planning-action model for exploring what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting human-robot teaching-learning process and AI-assisted educational techniques, illustrating the design guidelines and future research perspectives in intelligent tutoring robots.

Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.

This book is available free in .PDF format via the link above, and the site offers links to all the lab code. Written by professors at USC, Stanford and the University of Washington and focused on R -- the language of statistical computing that is often used for machine learning and AI programs in this area -- the book has been described as "the'how to' manual for statistical learning." Once you're done with this book, move on to the authors' follow-up, " The Elements of Statistical Learning," also available for free online (although both can be purchased, as well).

Salesforce Artificial Intelligence, Data Science & Data Discovery with Clicks Instead of Code / Salesforce Einstein AI This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. There is an AI and Data Discovery platform that can be constructed and configured with clicks instead of code. The disruptive power of this is that Artificial Intelligence is now available to the masses, and not just to the quants and data scientists among us. You can now not only catch the competition, but leap frog past them, by leveraging Salesforce Einstein as your On-Demand Data Scientist.

In this tutorial, you will learn how to colorize black and white images using OpenCV, Deep Learning, and Python. Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input (for example, an ocean on a clear sunny day must be plausibly "blue" -- it can't be colored "hot pink" by the model). The novel approach we are going to use here today instead relies on deep learning. We will utilize a Convolutional Neural Network capable of colorizing black and white images with results that can even "fool" humans! To learn how to perform black and white image coloration with OpenCV, just keep reading!

Learn the complete quant trading workflow and use machine learning algortihms to develop good trading strategies. The course is designed to fully immerse you into the complete quantitative trading workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

This is a step-by-step guide to build an image classifier. The AI model will be able to learn to label images. I use Python and Pytorch. When we write a program, it is a huge hassle manually coding every small action we perform. Sometimes, we want to use packages of code other people have already written.