The MOOC Replication Framework (MORF) is a novel software system for feature extraction, model training/testing, and evaluation of predictive dropout models in Massive Open Online Courses (MOOCs). MORF makes large-scale replication of complex machine-learned models tractable and accessible for researchers, and enables public research on privacy-protected data. It does so by focusing on the high-level operations of an extract-train-test-evaluate workflow, and enables researchers to encapsulate their implementations in portable, fully reproducible software containers which are executed on data with a known schema. MORF's workflow allows researchers to use data in analysis without providing them access to the underlying data directly, preserving privacy and data security. During execution, containers are sandboxed for security and data leakage and parallelized for efficiency, allowing researchers to create and test new models rapidly, on large-scale multi-institutional datasets that were previously inaccessible to most researchers. MORF is provided both as a Python API (the MORF Software), for institutions to use on their own MOOC data) or in a platform-as-a-service (PaaS) model with a web API and a high-performance computing environment (the MORF Platform).

The course is broad and pretty decent introductory course, but there is a number of presentation and course design flaws. First, while I'm not sure whether it is solely a Coursera's typical marketing approach to prevent users from refusing the course just because of the minimum amount of time required, or authors' unintended misestimations, but the actual time needed to complete the course is a way more than listed at the course home page, especially assignments. Often the time needed only to run an assignment training with no coding exceeds the given estimate. To get the value from the course one should be prepared to allocate much more time (2x-3x in total). Second, the course is too broad to be called an introductory one but too shallow in terms of math/practical/reasoning details to be named a deep one.

In this course, you'll learn about clustering and dimension reduction, the two fundamental techniques of unsupervised learning and you'll learn to apply them using Python 3 and industry standard, freely available software libraries like scikit-learn and SciPy. You're going to learn to use the fundamental tools of unsupervised learning that professional data scientists use everyday. So who is this course for? Perhaps you're an IT professional, an analyst, a scientist or an academic, and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. In this course I'm going to share with you not only what I learnt but also the joy and the fascination of discovering patterns in data - the wonder of finding hidden structure in datasets that seemed at first too large and too complex.

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It is very useful for day-to-day data analysis tasks. Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you've learned in R.

This week is entirely devoted to you learning how to use Simscape Fluids (formerly SimHydraulics), the fluid power simulation application that we use in the course. The lecture provides an introduction to computer-based, object-oriented simulation, and goes through a demo of using Simscape Fluids. The homework assignment contains the real work because this is where you will learn to use Simscape Fluids. The homework ends with an open-ended problem that encourages you to branch out on your own and create and run simulations based on examples listed in the course Simscape Fluids resource page or on any other fluid power system that interests you. We will be monitoring the discussion boards to help you with any technical problems with Simscape Fluids.

I have never regretted enrolling in the Inspiring Leadership through Emotional Intelligence course. It has indeed been a course that has provided me with new knowledge, ideas, and a broader perspective relating to;life in general. How could I be in a position to understand emotional, social and cognitive intelligence and their applicability in my personal life, work, and relationship? Not to mention dealing with chronic stress as a leader and the need for renewal. Professor Boyatzis is such an intelligent professor.

Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. For automatic washing machine,we can write programs for the washing machine functionality. All the materials for this course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or take decisions as DIY investor. Learning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for DIY investors' quantitative trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating fund historical data for back-testing to achieve greater effectiveness.