This is a follow-up tutorial article of our previous article entitled "Robot Basics: Representation, Rotation and Velocity". For better understanding of the topics covered in this articles, we recommend the readers to first read our previous tutorial article on robot basics. Specifically, in this article, we will cover some more advanced topics on robot kinematics, including robot motion, forward kinematics, inverse kinematics, and robot dynamics. For the topics, terminologies and notations introduced in the previous article, we will use them directly without re-introducing them again in this article. Also similar to the previous article, math and formulas will also be heavily used in this article as well (hope the readers are well prepared for the upcoming math bomb). After reading this article, readers should be able to have a deeper understanding about how robot motion, kinematics and dynamics. As to some more advanced topics about robot control, we will introduce them in the following tutorial articles for readers instead.

The course was updated on November 4th, 2022, entirely. The new content is a significant improvement to the old course, with a better course structure, more real-world apps, and using the latest version of Python and other recent programming tools. The course assumes you have never programmed before and teaches Python from zero. This is the only course that follows a multimodal learning approach that offers students both a video course and an environment that simulates real-world programming activities similar to a real bootcamp. Students learn Python by building programs from scratch, adding new features to existing programs, improving existing features, fixing bugs, engaging in code experiments, learning programming tools that every programmer should know, deploying apps in the cloud, and engaging with other fellow students.

One of the biggest threats to productivity in recent times is context switching. It is a term originating from computer science but applied to humans it refers to the process of stopping work on one thing, performing a different task, and then picking back up the initial task. During a work day, you might want to check something on Stack Overflow, for example, which normalization technique to choose for your project. While doing so, you start exploring the documentation of scikit-learn to see which approaches are already implemented and how they compare against each other. This might lead to you some interesting comparison articles on Medium or video tutorials on YouTube.

Artificial Intelligence (AI) is much in the news these days. As a concept, AI seems completely unrelated to the field of law. Although, AI and Law are intricately intertwined and are becoming more so each day. In this lecture, Professor Harry Surden – a former software engineer and leader of the emerging interdisciplinary field of AI and Law – will explore: What is Artificial Intelligence? How is law affecting Artificial Intelligence?

Arthur Samuel, an American pioneer in the field of computer gaming, artificial intelligence and machine learning defined Machine Learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". There are computer programs that can teach themselves to grow and change when exposed to new data. Machine Learning focuses on such programs. Both search the data to look for patterns. Data mining applications extract data for human comprehension and machine learning mines that data to find out patterns.

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. You'll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you'll learn how to handle missing data, a key real-world challenge.

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Text Analytics 2: Visualizing Natural Language Processing is a practical course. There are 3 modules in this course. In the first module, you will learn Text Analytics and Human Cognition, Measuring Linguistic Similarity, Topic Modelling, etc. The next lesson will cover how to visualize text analytics. The last section of this course covers how to apply text analytics to New Fields.

Strap yourself in: Programming Artificial Intelligence is about to click! Since making the official tutorials for Bolt on Unity's Learn Site, creating this course has been a dream of mine. In collaboration with Holistic3D, I took Penny's quintessential C# tutorial series The Beginner's Guide to Artificial Intelligence and adapted it to *drumroll*… Unity Visual Scripting! In this course, you're getting the best of both worlds: Learning content from a renowned expert on AI and computer science remixed, reconfigured, and riffed on by a creative artist and designer who has helped thousands learn visual scripting from the early years to today… that's me! Through an open-ended, practice based approach you will follow along as each step is revealed for you to recreate two game worlds created with Unity 2021.3.9

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.