Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, ML researchers who develop advanced methods rarely provide detailed tutorials showing how to apply these methods. Existing tutorials are often costly to participants, presume extensive programming knowledge, and are not tailored to specific application fields. In an attempt to democratize ML methods, we organized a year-long, free, online tutorial series targeted at teaching advanced natural language processing (NLP) methods to computational social science (CSS) scholars. Two organizers worked with fifteen subject matter experts to develop one-hour presentations with hands-on Python code for a range of ML methods and use cases, from data pre-processing to analyzing temporal variation of language change. Although live participation was more limited than expected, a comparison of pre- and post-tutorial surveys showed an increase in participants' perceived knowledge of almost one point on a 7-point Likert scale. Furthermore, participants asked thoughtful questions during tutorials and engaged readily with tutorial content afterwards, as demonstrated by 10K~total views of posted tutorial recordings. In this report, we summarize our organizational efforts and distill five principles for democratizing ML+X tutorials. We hope future organizers improve upon these principles and continue to lower barriers to developing ML skills for researchers of all fields.

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.

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.

The Korea Advanced Institute of Science and Technology (KAIST) was established in 1971 by the Korean government as the nation's first research-intensive graduate school for science, engineering and technology. It has now grown into one of the world's best universities, delivering top notch education and research programs for undergraduate and graduate students. KAIST encourages interdisciplinary and convergent research across a wide spectrum of disciplines, as well as strong collaborations with industry and global institutions.

For a Self-reliant India, to fulfil demand of highly skilled Artificial Intelligence and Cyber Security professionals in the country, Defence Institute of Advanced Technology, DIAT, Pune is conducting the nationwide Online Training and Certification Courses (OTCC) in Cyber Security, Artificial Intelligence & Machine Learning(AI & ML). The School of Computer Engineering and Mathematical Sciences of DIAT has completed two batches of these courses and more than 1600 candidates are successfully trained and certified. The 3rd batch of AI & ML course is on-going. Now DIAT is launching next batches of 16-weeks Online Course on Cyber Security, and 12-weeks Online Course on Artificial Intelligence & Machine Learning (AI & ML)in December 2022. The Graduating students, professionals, or any graduate person can apply for these courses.

At the end of each week, you'll reflect on your learning and plot. Next steps to apply what you've learned in your professional practice. This is an important part of the course that we hope you'll use as a roadmap to better manage your team's data science projects. Now let's talk about what this course is all about. The aim of the course is to equip executives with the knowledge that will enable them to work productively with data scientists.

Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students' questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.

Andrew Ng is one of the biggest names in Artificial Intelligence and Machine Learning, after team-founding and -leading stints at Google Brain, Baidu, and elsewhere, and as founder of Coursera and Landing AI. His online courses have attracted millions of views. AI has huge potential outside of consumer software and internet apps, he believes. I think the biggest potential of AI still lies ahead of us, to use it for all the other industries other than just consumer software and internet. But candidly, when I walk around everywhere from factories to hospitals, they just seek mentors.

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course?