The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw features with a large degree of redundancy. Specifically, in order to capture the underlying learning patterns in the content domain and the temporal nature of the clickstream data, we train a modified auto-encoder (AE) combined with the long short-term memory (LSTM) network to obtain a fixed-length embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTM-AE are not only more parsimonious but also more discriminative for our prediction task. Using simple supervised learning models, the learned features can improve the prediction accuracy by up to 17% compared with the supervised neural networks and reduce overfitting to the dominant low-performing group of students, specifically in the task of predicting students' performance. Our approach is generic in the sense that it is not restricted to a specific supervised learning model nor a specific prediction task for MOOC learning analytics.

In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.

We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.

There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

There are no hard and fast rules for learning such a complex topic. The beauty of online learning is that you get to choose what you lack and what excite you. For this part 1 of the series, I will review the maths and python fundamental courses that I had taken. Please note that these are my personal opinion which might or might not resonate with you. I like to give special mention to Data Science A-Z by Kirill Eremenko and the SuperDataScience Team.

The educational system is broken, and unfair. For decades, if not centuries, learning was limited by geography and having the means to continue with higher education. Online learning and massive open online courses (MOOCs) promised to address the inequities in education while extending its reach across all geographies. However, the online model simply paved over the older methods with technology, and perhaps even making things worse -- pushing course material to students, with no effective way to track how much they're learning, or even if they're paying attention. Now, artificial intelligence (AI) may have an answer for that, bringing learning and feedback in a very personal way to students.

Ricardo Diaz is a machine learning engineer. He works for a great company in Peru, and he's a graduate of no less than four Nanodegree programs! But just two years ago, it was a different story. He was still in Venezuela, struggling to learn new skills. He was short of money, and his prospects for making a full-time salary weren't great.

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

One of the most difficult things about learning a new skill is finding time to study. Being able to complete assignments in between meetings or while traveling can make all the difference in the ability to make regular progress. Unfortunately, none of the online courses in programming, data science and machine learning I've taken over this past year have great mobile solutions. Much of the work still requires a laptop. After a great deal of searching, I finally found a solution in two applications that allow users to run Jupyter notebooks and python terminal commands, both of which are common tools for completing machine learning tasks.

With the machine learning market size expected to grow from $1.03 Billion USD in 2016 to $8.81 Billion USD by 2022, it can almost be said that machine learning is taking over the world. With that, there is a growing need for professionals who know the ins and out of machine learning. According to Forbes, machine learning patents grew at a 34 percent Compound Annual Growth Rate (CAGR) between 2013 and 2017, which is the third-fastest growing category of all patents granted. Also, the International Data Corporation (IDC) forecasts that spending on AI and ML will increase from $12 Billion USD in 2017 to $57.6 Billion USD by 2021. Even Deloitte Global predicts that the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.