The linear algebra essential to data science, machine learning, and artificial intelligence is often overlooked as most introductory courses fail to display the big picture.

My course will be your complete guide to the theory and applications of supervised & unsupervised machine learning and predictive modeling using the R-programming language. Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY MACHINE LEARNING & PREDICTIVE MODELS (K-means, Random Forest, SVM, logistic regression, etc) in R (many R packages incl. This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (classification & regressions) and unsupervised clustering techniques. Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain. In this age of big data, companies across the globe use R to analyze big volumes of data for business and research.

The increased integration of autonomous robots in industrial settings requires not only that industrial robots display more "human-like" behaviors for efficient human-robot collaboration (HRC) but also regulations for safety and ethical viewpoint in the working environment. On the one hand, we need to build robots that can act autonomously in a given and sometimes unpredictable environment. On the other hand, we want the same robots to cooperate with humans to reach a common goal in a safe environment. In this workshop, we want to address some of the issues that are relevant for HRC in industrial settings, including its "human factors" such as lack of trust and adaptability to autonomous machines in collaborative tasks, accountability (legal rights and obligations), and especially the sentiment that robots are "stealing jobs." Whereas some hope that in this HRC endeavor (supposed to boost industrial and economic growth) humans will have a supervisory role, thus decreasing their physical and cognitive efforts, some others are concerned that autonomous robots will have a negative impact on our economic growth and pose a negative challenge for our culture.

The automotive industry has undergone a massive evolution in the past two centuries. In just 200 years, traveling has moved from bullock carts to autonomous cars. If we compare this time frame and the pace of change with other sectors, it will surely leave us in awe. Moving ahead, humankind is expecting a big turnaround in the self-driving car industry. A forecast suggests that by 2025, there will be approximately 8 million autonomous vehicles or semi-autonomous cars on the road.

Today, artificial intelligence innovation is all over the place. From online shopping to the data used for scholastic tasks. From sectors like automotive, manufacturing to finance and healthcare, AI has become an integral part of human life. Particularly with regards to financial investment, in addition to the fact that artificial intelligence allows you to settle on the right choices by looking over the feasible options available, but also opens unlimited opportunities for monetary escalation. In this segment, you will learn about the top AI stocks in 2022 to invest in.

Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand. But how do you get started? Maybe you tried to get started with Machine Learning, but couldn't find decent tutorials online to bring you up to speed, fast. Maybe the information you found was too basic, and didn't give you the real-world Machine learning skills using Python that you needed.

Description This course introduces machine learning covering the three main techniques used in industry: regression, classification, and clustering. It is designed to be self-contained, easy to approach, and fast to assimilate.

Artificial intelligence and machine learning are two popular and often hyped terms these days. And people often use them interchangeably to describe an intelligent software or system. But even though both AI and ML are based on statistics and mathematics, they are not the same thing. In this article, you will learn the differences between AI and ML with some practical examples to help clear up any confusion. Artificial intelligence, or AI, is the ability of a computer or machine to mimic or imitate human intelligent behavior and perform human-like tasks.

Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. This is especially true when a human operator is required to provide the ground truth - such a source should only be queried sparingly. In this work, we address the problem of curiosity as it relates to online, real-time, human-in-the-loop training of an object detection algorithm onboard a robotic platform, one where motion produces new views of the subject. We propose a deep reinforcement learning approach that decides when to ask the human user for ground truth, and when to move. Through a series of experiments, we demonstrate that our agent learns a movement and request policy that is at least 3x more effective at using human user interactions to train an object detector than untrained approaches, and is generalizable to a variety of subjects and environments.

Description Welcome, In this course, we aim to specialize in artificial intelligence by doing Machine Learning and Deep Learning Projects at various levels. Before starting the course, you must have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms and Python(3.8). This course was prepared in July 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.