E.g., 2016-07-24
E.g., 2016-07-24
E.g., 2016-07-24
Sep 5th 2016

Learn how to apply selected statistical and machine learning techniques and tools to analyse big data. Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it. So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics.

Average: 6 (1 vote)
Aug 2016

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.

Average: 5 (5 votes)
Jul 25th 2016

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5 (6 votes)
Jul 25th 2016

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: 10 (1 vote)
Jul 25th 2016

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

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Jul 25th 2016

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: 8.4 (17 votes)
Jul 25th 2016

Case Studies: Analyzing Sentiment & Loan Default Prediction
In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Average: 10 (1 vote)
Jul 25th 2016

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

No votes yet
Jul 18th 2016

Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

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Jul 18th 2016

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: 3.8 (5 votes)
Jul 13th 2016

Have you ever wanted to build a new musical instrument that responded to your gestures by making sound? Or create live visuals to accompany a dancer? Or create an interactive art installation that reacts to the movements or actions of an audience? If so, take this course! In this course, students will learn fundamental machine learning techniques that can be used to make sense of human gesture, musical audio, and other real-time data.

Average: 7 (2 votes)
Jul 11th 2016

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Average: 8 (4 votes)
Jul 11th 2016

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.

Average: 5.5 (2 votes)
Jul 11th 2016

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

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Jul 11th 2016

Learn how to use Hadoop technologies in Microsoft Azure HDInsight to create predictive analytics and machine learning solutions. Are you ready for big data science? In this course, learn how to implement predictive analytics solutions for big data using Apache Spark in Microsoft Azure HDInsight. You will learn how to work with Scala or Python to cleanse and transform data, build machine learning models with Spark MLlib (the machine learning library in Spark), and create real-time machine learning solutions using Spark Streaming. Plus, find out how to use R Server on Spark to work with data at scale in the R language.

Average: 10 (1 vote)
Jul 4th 2016

Create advanced data visualizations with Python, the language of data scientists, and build on your existing Python skills. This practical course, developed in partnership with Coding Dojo, targets individuals who have introductory level Python programming experience. The course teaches students how to start looking at data with the lens of a data scientist by applying efficient, well-known mining models in order to unearth useful intelligence, using Python, one of the popular languages for Data Scientists.

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Jun 27th 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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Jun 20th 2016

Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning. Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from MIT and Microsoft. In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization. Plus, look at examples of how to build a cloud data science solution using Azure Machine Learning, R, and Python.

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Apr 15th 2016

Learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Average: 9 (1 vote)
Mar 14th 2016

Aimed at a general business audience, this course demystifies artificial intelligence, provides an overview of a wide range of cognitive technologies, and offers a framework to help you understand their business implications. Some experts have called artificial intelligence "more important than anything since the industrial revolution."[i] That makes this course essential for professionals working in business, operations, strategy, IT, and other disciplines. Throughout the course, participants will build a knowledge base on cognitive technologies to equip them to engage in discussions with colleagues, customers, and suppliers and help them shape cognitive technology strategy in their organization.

Average: 8.6 (5 votes)

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