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E.g., 2017-06-22
E.g., 2017-06-22
Jun 26th 2017

How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.

Average: 8.5 (10 votes)
Jun 26th 2017

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Average: 6.5 (11 votes)
Jun 26th 2017

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Average: 7.6 (8 votes)
Jun 26th 2017

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, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Average: 7.5 (32 votes)
Jun 26th 2017

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Average: 7.2 (5 votes)
Jun 19th 2017

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Average: 5.5 (4 votes)
Jun 6th 2017

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life.

Average: 7.5 (2 votes)
Feb 27th 2017

Exploratory multivariate data analysis is studied and teached in a French-way since a long time in France. This course focuses on four essential and basic methods, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical and clustering. This course has been designed for scientists whose aim is not to become statisticians but who feel the need to analyze the data themselves. It is therefore addressed to practitioners who are confronted with the analysis of data in marketing, surveys, ecology, biology, geography, etc.

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Aug 15th 2016

Get hands-on experience building and deriving insights from machine learning models using R, Python, and Azure Machine Learning. Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

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A self-managed MOOC where you organize your own study schedule and learning journey (through the content provided). The amount of data generated by the information society is growing day by day, and will continue to grow thanks to the explosion of social networks, the smarts cities, the big data, mobile devices, sensors, etc. This exponential increase in the volume of data that are generated makes it imperative the use of systems that are capable of analysing and turn them into useful information. For this reason, our society, our businesses and institutions need intelligence at the moment to integrate within their organizational processes and decision, and this involves integrating tools of business analytics or smart data. This course provides an introduction to these tools of business intelligence, the associated main methodologies and current trends within this area.

Average: 9.5 (2 votes)
Mar 25th 2013

This course is about building `web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm.

Average: 4 (1 vote)