Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier's site to your web hosting service provider's support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definietely having its moment.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch.

Hyderabad: The Indian Institute of Technology Hyderabad (IIT-H) is going to collaborate with the Government of Telangana to carry out research in Artificial Intelligence. The Institute is partnering with the Information Technology, Electronics and Communication Department, for building, and identifying quality datasets, along with third parties such as Industry. The Memorandum of Understanding (MoU) was signed by Prof B S Murty, director, IIT Hyderabad, and Jayesh Ranjan, principal secretary to Government of Telangana, during an event held on January 2, 2020 as part of '2020: Declaring Telangana's Year of AI' initiative. They will also work on Education and Training to prepare and deliver content and curriculum on AI courses, which are to be delivered to college students along with industry participants. Prof Murty, director of IIT Hyderabad, said, "IIT Hyderabad is a pioneer in the field of Artificial Intelligence. IIT Hyderabad is the first institute in the country to launch a BTech program in AI. With a strong base of close to 25 faculty in the Department of AI, IIT Hyderabad is looking forward to such opportunities to come up with many more innovations and research achievements in the field."

Education is the foundation any country is built upon. It is through education that our children can reach their full potential, both as principled, responsible citizens, and as being productive members of the country. But, how is it possible to build an education system which best develops the adults of tomorrow? It's not an easy task to build schools which will cater to the needs of those in the fast-changing world of the 21st Century. Education is the ability to pass knowledge.

There's no doubt that Artificial Intelligence has become a major buzzword these days. You might already be aware of it as this trailblazing technology that has made captivating headlines in terms of the innovations that it brings along and the risks that it poses. Be it a self-driving car, a sex robot that can breathe, or a healthcare tool that detects possible breast cancer better than the experts, Artificial Intelligence is dominating almost every industry that we can think of. It has remained one of the biggest stories in tech in a course of the last few years and we wanted to know what its future holds. To see a picture beyond the mainstream AI, we interviewed Karan Bajaj, Founder and CEO, WhiteHat Jr, who shared with us his insight around Machine Learning, need for ethics around AI, and AI's impact on education.

Thomas Edison famously said that success is 90% perspiration and 10% inspiration. Even though the hype around artificial intelligence has never been higher, the reality of what it takes to actually work in the field - and what it takes to use it successfully - is mired in confusion. Indeed, hype makes it look like 100% inspiration; it hides the work involved in building knowledge and learning skills. So, to help tackle that, here are the 3 important elements to machine learning that might structure how you'd approach it. This is the one thing that people overlook.

In this paper, we propose a new metric to measure goodness-of-fit for classifiers, the Real World Cost function. This metric factors in information about a real world problem, such as financial impact, that other measures like accuracy or F1 do not. This metric is also more directly interpretable for users. To optimize for this metric, we introduce the Real-World- Weight Crossentropy loss function, in both binary and single-label classification variants. Both variants allow direct input of real world costs as weights. For single-label, multicategory classification, our loss function also allows direct penalization of probabilistic false positives, weighted by label, during the training of a machine learning model. We compare the design of our loss function to the binary crossentropy and categorical crossentropy functions, as well as their weighted variants, to discuss the potential for improvement in handling a variety of known shortcomings of machine learning, ranging from imbalanced classes to medical diagnostic error to reinforcement of social bias. We create scenarios that emulate those issues using the MNIST data set and demonstrate empirical results of our new loss function. Finally, we sketch a proof of this function based on Maximum Likelihood Estimation and discuss future directions.

One of the factors often credited for this latest boom in artificial intelligence (AI) investment, research, and related cognitive technologies, is the emergence of deep learning neural networks as an evolution of machine algorithms, as well as the corresponding large volume of big data and computing power that makes deep learning a practical reality. While deep learning has been extremely popular and has shown real ability to solve many machine learning problems, deep learning is just one approach to machine learning (ML), that while having proven much capability across a wide range of problem areas, is still just one of many practical approaches.

So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? We gathered best industry professionals with tons of completed projects behind.