In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to use MATLAB to automatically generate code so you can learn syntax as you explore. You will also use interactive documents, called live scripts, to capture the steps of your analysis, communicate the results, and provide interactive controls allowing others to experiment by selecting groups of data. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background is required. To be successful in this course, you should have some knowledge of basic statistics (e.g., histograms, averages, standard deviation, curve fitting, interpolation). By the end of this course, you will be able to load data into MATLAB, prepare it for analysis, visualize it, perform basic computations, and communicate your results to others. In your last assignment, you will combine these skills to assess damages following a severe weather event and communicate a polished recommendation based on your analysis of the data. You will be able to visualize the location of these events on a geographic map and create sliding controls allowing you to quickly visualize how a phenomenon changes over time.

About this Course This four-week course titled AI and Law explores the way in which the increasing use of artificially intelligent technologies (AI) affects the practice and administration of law defined in a broad sense. Subject matters discussed include the connection be between AI and Law in the context of legal responsibility, law-making, law-enforcing, criminal law, the medical sector and intellectual property law. The course aims to equip members of the general public with an elementary ability to understand the meaningful potential of AI for their own lives. The course also aims to enable members of the general public to understand the consequences of using AI and to allow them to interact with AIs in a responsible, helpful, conscientious way. Please note that the law and content presented in this course is current as of the launch date of this course. At the end of this course, you will have a basic understanding of how to: • Understand the legal significance of the artificially intelligent software and hardware. • Understand the impact of the emergence of artificial intelligence on the application and administration of law in the public sector in connection with the enforcement of criminal law, the modelling of law and in the context of administrative law. • Understand the legal relevance of the use of artificially intelligent software in the private sector in connection with innovation and associated intellectual property rights, in the financial services sector and when predicting outcomes of legal proceedings. • Understand the importance of artificial intelligence for selected legal fields, including labour law, competition law and health law. Syllabus and Format The course consists of four modules where one module represents about one week of part-time studies. A module includes a number of lectures and readings, and finishes with an assessment – a quiz and/or a peer graded assignment. The assessments are intended to encourage learning and ensure that you understand the material of the course. Participating in forum discussions is voluntary. Modules Module 1. AI and Law Module 2. Legal AI in the Public Sector Module 3. Legal AI in the Private Sector Module 4. Selected Challenges Lund University Lund University was founded in 1666 and has for a number of years been ranked among the world’s top 100 universities. The University has 47 700 students and 7 500 staff based in Lund, Sweden. Lund University unites tradition with a modern, dynamic, and highly international profile. With eight different faculties and numerous research centers and specialized institutes, Lund is the strongest research university in Sweden and one of Scandinavia's largest institutions for education and research. The university annually attracts a large number of international students and offers a wide range of courses and programmes taught in English. The Faulty of Law is one of Lund University’s four original faculties, dating back to 1666. It is a modern faculty with an international profile, welcoming both international and Swedish students. Education, research and interaction with the surrounding community are the main focus of the Faculty’s work. The connection between the three is particularly apparent in the programmes and courses offered by the university, including the university’s MOOC course in European Business Law. The students get the chance to engross themselves in traditional legal studies, while interacting with both researchers and professionally active lawyers with qualifications and experience from various areas of law. The faculty offers three international Masters: two 2-year Master’s programmes in International Human Rights Law and European Business Law, and a 1-year Master’s in European and International Tax Law. Students from around 40 countries take part in the programmes which offer a unique subject specialization within each field, with highly qualified researchers and professional legal practitioners engaged in the teaching.

In this guided project, we are going to create a neural network and train it on a small dataset of superhero names to learn to generate similar names. The dataset has over 9000 names of superheroes, supervillains and other fictional characters from a number of different comic books, TV shows and movies. Text generation is a common natural language processing task. We will create a character level language model that will predict the next character for a given input sequence. In order to get a new predicted superhero name, we will need to give our model a seed input - this can be a single character or a sequence of characters, and the model will then generate the next character that it predicts should after the input sequence. This character is then added to the seed input to create a new input, which is then used again to generate the next character, and so on. You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Recurrent Neural Networks, and optimization algorithms like gradient descent but want to understand how to use the TensorFlow to start performing natural language processing tasks like text classification or text generation. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

The goal of this course is to understand the foundations of Big Data and the data that is being generated in the health domain and how the use of technology would help to integrate and exploit all those data to extract meaningful information that can be later used in different sectors of the health domain from physicians to management, from patients to caregivers, etc. The course offers a high-level perspective of the importance of the medical context within the European context, the types of data that are managed in the health (clinical) context, the challenges to be addressed in the mining of unstructured medical data (text and image) as well as the opportunities from the analytical point of view with an introduction to the basics of data analytics field.

In this 1 hour long project-based course, we’ll learn to create a mapping data flow on the azure data factory. First, we’ll learn to create an azure data factory on the Azure portal. Then we’ll learn to create an azure storage account so that we could store the source data on the blob containers. We’ll learn to configure the source and the sink transformation. We’ll learn to work with basic data flow transformations such as select, filters, sort, joins , derived columns, and conditional split transformations. We’ll learn to create a simple mapping data flow in the azure data factory. We’ll also learn to create and combine multiple streams of data on mapping data flows. Finally, we’ll also learn to store the transformed data to the destination. You must have an Azure account prior.

In this 1.5-hour long project, you will learn how to clean and preprocess geolocation data for clustering. You will learn how to export this data into an interactive file that can be better understood for the data. You will learn how to cluster initially with a K-Means approach, before using a more complicated density-based algorithm, DBSCAN. We will discuss how to evaluate these models, and offer improvements to DBSCAN with the introduction of HDBSCAN. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a Google Cloud Self-Paced Lab. In this lab you will use native derived tables to answer complex questions with LookML.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use the APIs Explorer tool to create Cloud Storage buckets, upload data to the bucket, and remove content from buckets.

This is a self-paced lab that takes place in the Google Cloud console. A step-by-step guide through the process to configure multiple methods to ingest Google Cloud data into Splunk. In this hands-on lab you'll learn how to configure Google Cloud to send logging and other infrastructure data to Splunk Cloud via Dataflow, the Splunk Add-on for Google Cloud Platform, and Splunk Connect for Kubernetes (SC4K).