Machine Learning

 

 


 

Customize your search:

E.g., 2017-09-20
E.g., 2017-09-20
E.g., 2017-09-20
Self Paced

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: 7.3 (4 votes)
Self Paced

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.

Average: 8.8 (6 votes)
Self Paced

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.

No votes yet

Self Paced

Traverse the data analysis pipeline using advanced visualizations in Python, and make machine learning start working for you.

Average: 9.1 (7 votes)
Self Paced

Explore theory and practice, and work with tools like R, Python, and Azure Machine Learning to solve advanced data science problems. In this data science course, you will explore the theory and practice of select advanced methods commonly used in data science.

Average: 4.5 (2 votes)
Sep 25th 2017

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.

Average: 7.8 (12 votes)

Sep 25th 2017

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Average: 7.5 (4 votes)
Sep 25th 2017

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!

Average: 7.4 (5 votes)
Sep 25th 2017

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: 6.6 (19 votes)
Sep 25th 2017

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.

No votes yet
Sep 25th 2017

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: 5.9 (18 votes)

Sep 25th 2017

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems.

No votes yet