This course is for every beginner in the data science space. We have been there before and we understood what your learning challenges are. This short course will focus on showing you end to end what it takes to build and deploy a simple machine learning solution. You will be able to deploy this solution using the flask framework as an API and also as a Platform. We will also introduce you to libraries that make it easy to quickly explore, build, and deploy a machine learning solution.

Artificial Intelligence (AI) is becoming an important component of various sectors and in decision-making in various domains. Research in AI has seen tremendous growth, thanks to big data, escalated processing speed, and innovations in AI-based models. McKinsey Global Institute predicts that by 2030, at least 70 percent of companies will have to adopt at least one type of AI technology and around 60 percent of the current occupations will be automated in the next ten years. Recognizing the importance of AI in almost every field, many countries have regarded AI as a national priority. To promote AI and the research involved, the USA launched the American Artificial Intelligence Initiative in 2019.

Artificial Intelligence (AI) revolution is here! "Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43. Deep Learning, one of the segments analyzed and sized in this study, displays the potential to grow at over 42. AI is a broader general field that entails several subfield such as machine learning, robotics, and computer vision. For companies to become competitive and skyrocket their growth, they need to leverage Artificial Intelligence (AI) power to improve processes, reduce cost and increase revenue. AI is broadly implemented in many sectors nowadays and has been transforming every industry from banking to healthcare, transportation and technology. The demand for AI talent has exponentially increased in recent years and it's no longer limited to Silicon Valley! According to Forbes, AI Skills are among the most in-demand for 2020 [2]. The purpose of this course is to provide you with knowledge of key aspects of modern Artificial Intelligence applications in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. One key unique feature of this course is that we will be training and deploying models using Tensorflow and AWS SageMaker. In addition, we will cover various elements of the AI/ML workflow covering model building, training, hyper-parameters Deploy Emotion AI-based model using Tensorflow 2.0 Serving and use the model to make inference. Understand the concept of Explainable AI and uncover the blackbox nature of Artificial Neural Networks and visualize their hidden layers using GradCam technique. Develop Deep Learning model to automate and optimize the brain tumor detection processes at a hospital. Build and train AI model to detect and localize brain tumors using ResNets and ResUnet networks (Healthcare applications). Build, train, deploy AI models in business to predict customer default on credit card using AWS SageMaker XGBoost algorithm. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0. Develop ANNs models and train them in Google's Colab while leveraging the power of GPUs and TPUs. Artificial Intelligence (AI) revolution is here! "Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43.

Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.

Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of "forgetting" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Memory (BAM), that takes advantage of past experience by allowing the agent to choose which past observations to remember and which to forget. We demonstrate that BAM generalizes many popular Bayesian update rules for non-stationary environments. Through a variety of experiments, we demonstrate the ability of BAM to continuously adapt in an ever-changing world.

What is a neural network? A Neural Network is a system inspired by the human brain that is designed to recognize patterns. Simply put it is a mathematical function that maps a given input in conjunction with information from other nodes to develop an output. What can a neural network do? How does a neural network work?

In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance. We've come a long way from that time. The internet is still a dark, dreadful place, but it's much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligence-based website creation tools that are dominating the digital marketing industry.

Welcome to experience "Artificial Intelligence in Digital Marketing - Gold Edition 2022." Artificial Intelligence has transformed the virtual panorama, inclusive of Google's RankBrain personalising suggestions by Amazon. Artificial Intelligence (AI) is hastily turning into important in the daily happenings of the virtual global, with marketing and advertising and marketing being no exception. The idea of AI may also bring to thoughts 60's sci-fi with futuristic robots, however, it's definitely approximately so much greater. With the right understanding and evaluation of data and input, AI is playing an essential position in figuring out marketing trends. Brands and marketers are incorporating Machine Learning and Artificial Intelligence to save time and assets.

We present and analyze the employment of the Diproche system, a natural language proof checker, within a one-semester mathematics beginners lecture with 228 participants. The system is used to check the students' solution attempts to proving exercises in Boolean set theory and elementary number theory and to give them immediate feedback. The benefits of the employment of the system are assessed via a questionnaire at the end of the semester and via analyzing the solution attempts of a subgroup of the students. Based on our results we develop approaches for future improvements.

In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain. We also consider the concept of embodied conversational agents, briefly reviewing aspects such as character animation and speech processing. The many different approaches for representing dialogue in CAs are discussed in some detail, along with methods for evaluating such agents, emphasizing the important topics of accountability and interpretability. A brief historical overview is given, followed by an extensive overview of various applications, especially in the fields of health and education. We end the chapter by discussing benefits and potential risks regarding the societal impact of current and future CA technology.