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).

We are going to commence our new tutorial series about Machine Learning. We will use python as programming language for this tutorial series. This article gives a brief introduction to Machine Learning. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Machine Learning models consists algorithms that can learn and make predictions from data.Machine Learning has been evolved from prediction making and computational learning theory in artificial intelligence.It helps computers to learn and perform a certain task based on past experience.These models can be based on following: In this article we saw a brief introduction to Machine Learning and in next article we will see how to install Anaconda.

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.

Oftentimes when people think of artificial intelligence, images of robots, Skynet and a central command center immediately come to my mind. I think of that scene from Terminator 2, when a heavily armed robot crushes a human skull beneath his feet. He's controlled by another robot, who calculates every human move with efficiency and precision. But that's not really what AI is. AI is more complex than that.

Online learning may refer to the ones with batch size to be 1, but here I mean online reinforcement learning is the RL where the agent is updated at every timestep. Naively speaking, the concept of online reinforcement learning sounds very much like how human learns, and it's very effective for tasks like stochastic games. Since it performs an update at each timestep, the agent may be more robust under the circumstances such that the current state is relatively unfamiliar. As it was updated in the past ten or so timesteps which are close to the current timesteps, the agent is more adapted to the unfamiliar current states. Also, the weights of the agent may be considered to be conditioned on the past events in the same episode, which may alleviate the issue of LSTM and memory network, that is, they still has the limit on the extent to which they can remember the distant past events in the same episode.

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.

Test datasets are small contrived datasets that let you test a machine learning algorithm or test harness. The data from test datasets have well-defined properties, such as linearly or non-linearity, that allow you to explore specific algorithm behavior. The scikit-learn Python library provides a suite of functions for generating samples from configurable test problems for regression and classification. In this tutorial, you will discover test problems and how to use them in Python with scikit-learn. A problem when developing and implementing machine learning algorithms is how do you know whether you have implemented them correctly.

What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity.