Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.

British-born Andrew Ng has had a rich career in the technology industry as Co-Founder and Head of Google Brain, former Chief Scientist at Baidu and Co-Founder of Coursera. At Baidu, Ng built the company's artificial intelligence (AI) sector into a team of several people. In an interview with Lex Fridman, Ng shared where his passion for the industry started: " Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old. At that time I was learning the BASIC programming language and they would take these folks and they'll tell you type this program into your computer." "So I typed out programs on my computer and as the result of all the typing, I would get to play these very simple, shoot them up games that I had implemented on my little computer. So I thought it was fascinating as a young kid that I could write this code. I was really just copying code from a book into my computer to then play these cool little video games. Another moment for me was when I was a teenager and my father was a doctor was reading about expert systems and about neural networks. So he got me to read some of these books and I thought it was really cool that you could write a computer that started to exhibit intelligence." he continued.

In this course, you'll be learning various supervised ML algorithms and prediction tasks applied to different data. You'll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.

This Professional Certificate is for anyone who wants to develop job-ready skills, tools, and a portfolio for an entry-level data engineer position. Throughout the self-paced online courses, you will immerse yourself in the role of a data engineer and acquire the essential skills you need to work with a range of tools and databases to design, deploy, and manage structured and unstructured data. By the end of this Professional Certificate, you will be able to explain and perform the key tasks required in a data engineering role. You will use the Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You will work with Relational Databases (RDBMS) and query data using SQL statements.

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly.

Andrew Ng's Machine Learning course has been revamped and updated and according to student ratings is better than ever.

Artificial intelligence (AI), robotics, automation – as well as less technologically-driven disruptions such as pandemics – mean the way our children and grandchildren are working will look very different to how we work today. We don't even have to look that far ahead to see change on a dramatic scale. It's been predicted that 85% of the jobs that will be available in 2030 don't yet exist! Factors such as the widespread shift to remote working, the emergence of the gig economy, and employees' increasing expectations of flexibility in their relationship with their employers will also play their part. Adding seismic shifts such as the great resignation into the mix means companies are frantically searching for new strategies when it comes to hiring and retaining talent.

Andrew Ng's DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The 3-course program is a new version of Ng's pioneering machine learning course, taken by over 4.8 million learners since 2012. The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. The new Machine Learning Specialization by @DeepLearningAI_ & @StanfordOnline is now available on @Coursera!

Do you want to learn Tensorflow and looking for the Best Resources to Learn Tensorflow?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Tensorflow including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Tensorflow. You can bookmark this article so that you can refer to this article later.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout.