About this course: Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study.

In Pandas Data Cleaning and Modeling with Python LiveLessons, Daniel Y. Chen builds upon the foundation he built in Pandas Data Analysis with Python Fundamentals LiveLessons. In this LiveLesson Dan teaches you the techniques and skills you need to know to be able to clean and process your data. Dan shows you how to do data munging using some of the built-in Python libraries that can be used to clean data loaded into Pandas. Once your data is clean you are going to want to analyze it, so next Dan introduces you to other libraries that are used for model fitting. Daniel Y. Chen is a graduate student in the interdisciplinary Ph.D. program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech.

Kevin Kelly has stated that AI is going to make even bigger changes to the economy than the internet. If you missed out on the internet dot com bubble of the late 90s and early 2000s, now is your chance. AI is coming, and it's coming fast. And AI is also one of the most difficult things to learn. Once you get past a Wikipedia article the next step is a 1000 page textbook that will take a year to read plus learning a bunch of new fields of math and programming.

Python is a multi-domain, interpreted programming language. It is a widely used general-purpose, high-level programming language. It is often used as a scripting language because of its forgiving syntax and compatibility with a wide variety of different eco-systems. Its flexible syntax enables developers to write short scripts while at the same time being able to use object-oriented concepts to develop very large projects. This course follows a task-based approach to help you create beautiful and very effective GUIs with the least amount of code necessary.

Effective visualization can help you get better insights from your data, and help you make better and more informed business decisions. This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As you will go through the course, you will get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, we'll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.

Many of the biggest names in the technology industry are consumed with developing an artificial general intelligence, or AGI. Unlike today's leading artificial intelligence software, an AGI wouldn't need flesh-and-blood trainers to figure out how to translate English to Mandarin or spot tumors in an X-ray. In theory, it would have some measure of independence from its creators, solve complex, novel problems on its own, and herald an era in which humankind is no longer superior to machines. The consensus among our pitiful fleshbrains is that if humans ever manage to create an AGI, it'll arise in Mountain View, Calif., Beijing, or Moscow. All three cities are near world-class AI research universities and are home to companies that have pumped billions into the AGI race. There exists, however, a chance that the breakthrough will come from the Swiss city of Lugano. The picturesque slice of Switzerland's southern tip is home to about 60,000 people, including a computer scientist named Jürgen Schmidhuber. He's a professor, a researcher, and the co-founder of a 25-employee AI startup called Nnaisense.

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. In this course, you'll start by learning how to acquire data from the web in its already "clean" format, such as in a .csv You'll then learn to transform this data so it's in its most useful format for analysis. After that, you'll dive into data aggregation and grouping, where you'll learn to group similar data for easier analysis purposes. From there, you'll be shown different methods of web scraping using Python.

Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. For instance, conditioned on the input signal, the output of a MASO DN can be written as a simple affine transformation of the input. This implies that a DN constructs a set of signal-dependent, class-specific templates against which the signal is compared via a simple inner product; we explore the links to the classical theory of optimal classification via matched filters and the effects of data memorization. Going further, we propose a simple penalty term that can be added to the cost function of any DN learning algorithm to force the templates to be orthogonal with each other; this leads to significantly improved classifi- cation performance and reduced overfitting with no change to the DN architecture. The spline partition of the input signal space that is implicitly induced by a MASO directly links DNs to the theory of vector quantization (VQ) and K-means clustering, which opens up new geometric avenue to study how DNs organize signals in a hierarchical fashion. To validate the utility of the VQ interpretation, we develop and validate a new distance metric for signals and images that quantifies the difference between their VQ encodings. (This paper is a significantly expanded version of a paper with the same title that will appear at ICML 2018.)

About this course: This capstone project course will give you a taste of what data scientists go through in real life when working with data. You will learn about why data cleaning and munging is an important part of data science and how it occupies more than 80% of a data scientist's daily work. You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code.