Excel for Data Analysis and Visualization This is an Archived Course EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be all available. Check back often to see when new course start dates are announced. Microsoft Excel is one of the most widely used solutions for analyzing and visualizing data. Beginning with Excel 2010, new tools were introduced to enable the analysis of more data, resulting in less time spent creating and maintaining the solutions and enabling a better understanding of what the data means. David MacKay: Information Theory, Inference, and Learning Algorithms: The Book Download the book too You can browse and search the book on Google books. You may download The book in one file (640 pages):
Selection: Machine Learning Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars,... 3 March 2014, 10 weeks
GET search/tweets Returns a collection of relevant Tweets matching a specified query. Please note that Twitter’s search service and, by extension, the Search API is not meant to be an exhaustive source of Tweets. Not all Tweets will be indexed or made available via the search interface. In API v1.1, the response format of the Search API has been improved to return Tweet objects more similar to the objects you’ll find across the REST API and platform.
Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.
Data Visualization from Coursera Data Visualization is the fifth and final course in the data mining specialization offered by John Hopkins University on Coursera. The 4-week course provides a high-level overview of data visualization, covering topics like human visual perception, basic plotting constructs and design principles, visualizing networks a Read More Data Visualization is the fifth and final course in the data mining specialization offered by John Hopkins University on Coursera. Jupyter Notebook Viewer Probabilistic Programming & Bayesian Methods for Hackers¶ Using Python and PyMC¶ The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples.
Neural Networks and Deep Learning, free online book (draft) A free online book explaining the core ideas behind artificial neural networks and deep learning (draft), with new chapters added every 2-3 months. By Gregory Piatetsky, @kdnuggets, Sep 20, 2014. Here is a Machine Learning gem I found on the web: Data Science Cheat Sheet I promised to write it long ago: here we go! Click on this link to see the most current version. I will update this article regularly. An old version can be found here and has many interesting links. All the material presented here is not in the old version. Professor TL McCluskey - Profile Vallati, M., Hutter, F., Chrpa, L. and McCluskey, T. (2015) ‘On the Effective Configuration of Planning Domain Models’. In: International Joint Conference on Artificial Intelligence, 25th - 31st July, 2015, Argentina Chrpa, L., Vallati, M. and McCluskey, T. (2015) ‘On the Online Generation of Effective Macro-operators’. In: International Joint Conference on Artificial Intelligence (IJCAI) 2015, 25th - 31st July, 2015, Buenos Aires, Argentina
Exploratory Data Analysis from Coursera The first 2 weeks of the course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal components analysis and single value decomposition only jump back to p Read More The first 2 weeks of the course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal components analysis and single value decomposition only jump back to plotting with a section on color. The clustering section seems a little about of place since there is not any introduction explaining the purpose of clustering. What's worse the SVD and PCA sections require a fairly high level of linear algebra knowledge to understand, which are not prerequisites for this course.
Introduction to Statistical Learning An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani