Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.

Are you ready to take the PCEP – Certified Entry-Level Python Programmer exam? The first two exams are in the form of practice tests and consists of 200 questions that may appear during the Certified Entry-Level Python Programmer exam. Where necessary, explanations are added to the questions. This course allows you to confirm your proficiency and give you the confidence you need to earn the PCEP – Certified Entry-Level Python Programmer certification. PCEP – Certified Entry-Level Python Programmer certification shows that the individual is familiar with universal computer programming concepts like data types, containers, functions, conditions, loops, as well as Python programming language syntax, semantics, and the runtime environment.

Are you looking for Best Data Science Degree Online? This Online Data Science Course list will help you to become a top Data Scientist. Data science or data-driven science is one of today's fastest-growing fields. Do you want to become a Data Scientist in 2022? The list of the Data Science Degrees will give you a clear idea from data science definition to expert levels. If you don't know how to get a data scientist certification then this data science certificate program online will help you to get an online data science certificate. You will be able to get Microsoft data science certification or even a Harvard data science certificate with this excellent collection of online courses. Also, this Data Science training will give you an idea about data science, python, data scientist, big data, analytics, machine learning, deep learning, and Artificial Intelligence (AI) which are the most booming topics now. You can be a data science master in a short period. All big companies, publishers, advertisers, and other industries are now highly dependent on data science or machine learning. So, it is high time to learn some skills in data science, for example, get the highly demanded Data Science online certifications. How does it work at present, and why data scientists' careers and data science jobs are in top positions? If you like a trendy career, you have that opportunity right now and get hired by the big industries. At the same time, online entrepreneurs and business personnel also need to update themselves with fundamental machine learning skills to compete with the fast-moving industry. Below are a few best Data Science online courses that might assist you to jump-start your knowledge of the data science sector. If you want to learn machine learning, then this is the perfect course for you. Two professional data scientists designed this course so that you can learn the theory and algorithms behind machine learning. If you just learn the coding libraries, then you will not know what is going on in the back end. You will not be able to perform well in the industries. This is why this is a very good course to get started in the machine learning world. The course also includes study materials about coding libraries. The two data scientist professionals walk you through the course step by step. Even if you are quite familiar with data science, this is going to help you learn a lot more new things. The course has been structured in a very friendly way.

This course can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. In this course, you'll be learning about Computer Vision as a field of study and research. First we'll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective.

Artificial intelligence is touching every aspect of how we engage with information (and much more) these days. Today, a startup building out a business based on one particular application of that -- how to apply AI to knowledge management in the workplace -- is announcing some funding as it finds some decent traction for its approach. Sana Labs -- which provides an AI-based platform to help people manage information at work, and subsequently to use that data as a resource for e-learning within the organization -- has closed a round of $34 million after seeing ARR grow seven-fold in the last year. Menlo Ventures, the U.S. VC firm, is leading the round for Stockholm-based Sana, with EQT Ventures and a whopping 25 angels and founder/operator individuals also participating. This is a Series B that values Sana at $180 million post-money.

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course.

An online course with an AI tutor achieves a significantly higher completion rate than traditional online courses thanks to a personalized learning experience. Does Artificial Intelligence enable quality education for everyone? Artificial Intelligence can revolutionize education: Instead of mass teaching, AI tutors could provide personalized, active and hands-on learning experiences. This can be a useful complement to the crowded classroom, the dry lecture, and especially in combination with online courses, which are used by millions of people around the world to learn every day. The latter scenario in particular plays a central role in the plans of learning platform startup Korbit, as only a small portion of humanity has access to quality education.

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.

Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.