Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike. To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. This trend is unlikely to slow down anytime soon: The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion by 2023, a CAGR of 40.1 percent. Business and technology-related courses make up 40 percent of these online courses. Many universities have also joined the drive to fill the AI leadership gap by offering high-touch executive education programs.

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve as a web app using Microsoft Azure Web App Services. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline on Google Kubernetes Engine. Previously we demonstrated how to deploy a ML pipeline on Heroku PaaS and how to deploy a ML pipeline on Azure Web Services with a Docker container.

The University of Tennessee, Knoxville, is among the 14 member universities of the Southeastern Conference forming a new artificial intelligence consortium. The consortium is designed to grow opportunities in the fast-changing fields of AI and data science, which are expected to be foundational for the future of industry, education, and research. It is believed to be the first athletics conference collaboration to focus on AI for workforce development. "Artificial intelligence and data science have universal applications, and we are proud to join with SEC universities to lead education and research in these emerging fields," said Provost and Senior Vice Chancellor John Zomchick. "The consortium will create exciting opportunities for our faculty, staff, and students to explore AI and data science and shape their future."

Books and courses where two main resources that I used to get to where I am right now. Among all the resources in this section, Kaggle stands out as one of the most amazing platforms on the internet for practicing and learning machine learning! Kaggle hosts lots of data science and deep learning competitions every year with participants taking part from all over the world. There are also loads of invaluable datasets on Kaggle that you can use to train your own models, all for free. The other amazing part of this platform is where people can share their code and solution to the competitions, all in a simple Jupyter notebook which makes experimenting with the code really easy.

We all know about Artificial Intelligence (AI), and how it has revolutionized our daily lives. From translating languages to summarising lengthy books to self-driving automobiles-- It's all downright exceptional, Thanks to the advancement done in AI. The history of Deep Learning dates back to 1943 when Walter Pitts and Warren McCulloch created a model based on the neural networks of the human brain. Deep Learning kept progressing since then and amazingly, it still is expanding! Deep learning is the subset of Machine Learning -- in other words'Advanced Machine Learning, but that begs the question -- What is Machine Learning?

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series of articles, I will go through the basic techniques to work with time-series data, starting with data manipulation, analysis, and visualization to understand your data and prepare it for and then using statistical, machine learning, and deep learning techniques for forecasting and classification. It will be more of a practical guide in which I will be applying each discussed and explained concept to real data. All the codes and datasets used in this article can be found in this repository. In this section, we will learn how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots.

Learn about different Activation Functions used in a Neural Network. Implement Artificial Neural Networks to solve real world Problems. Implement the LSTMs to solve Sequential Problems. Learn about different Activation Functions used in a Neural Network. Implement Artificial Neural Networks to solve real world Problems.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award winning books on games AI.

As an Associate Data Engineer, you will be part of the ambitious effort in building next generation products at Euromonitor International. You will be a part of the team which creates and manages a large fleet of web crawling robots that collects data from hundreds of websites. You will learn the intricacies of modern web infrastructure, how dynamic websites work and become an expert in web crawling and scraping. We expect you to have some experience in computer programming and to be familiar with HTML, Javascript, jQuery and CSS selectors. We would also welcome those interested or with some experience in FMCG market research. We offer an international, growing and challenging working environment with genuine global career development opportunities.

Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work. Welcome to the "Python Programming: Machine Learning, Deep Learning Python" course. In this course, we will learn what is Deep Learning and how does it work.