Tag Archives: free

Deep Learning Coursera Specialization

Andrew Ng, co-founder of Coursera and Deep Learning Expert, is launching a new specialization on Coursera. Details can be found at DeepLearning.ai or the Deep Learning Specialization Page. The specialization consists of 5 courses. They are free to audit and watch the videos. There is a fee to get graded assignments and receive a certificate of completion. The first course just started this week, so it is great time to start learning some deep learning.

Good Luck!

A Data Science Career with Kirk Borne, Free Webinar

Once again, The Data Incubator, is hosting another Data Science in 30 minutes webinar. This one features the career of Kirk Borne.

Renowned data scientist, Kirk Borne will take viewers on a journey through his career in science and technology explaining how the industry-and himself have evolved over the last 4 decades. Starting with skipping lunches in high school to a systematic twitter obsession, Kirk will shed light on his road to success in the data science industry.

Kirk is universally considered one of the most (if not the most) influential voices in data science. If you are interested in a career in data science, this is a webinar you will not want to miss.

The webinar is 5:30 Eastern Time on August 29, 2017, and registrations are currently being accepted. It is free.

Netflix Data Scientist on Machine Learning: Free Webinar

The Data Incubator, a data science fellowship program, is currently running a Data Science in 30 minutes webinar series. Next week features a free webinar with Dr. Becky Tucker of Netflix. Dr. Tucker is a Senior Data Scientist at Netflix where she specializes in predictive modeling for content demand (think what do people want to watch). The full abstract of the webinar is below. The webinar is free; all you need to do is register.

Predicting Content Demand with Machine Learning

Date/Time: March 9, 2017 @ 5:30 PM ET
Location: Online
Register: Click Here

Abstract: Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that – from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk will cover work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and predictive models.

Update – Below is the Recorded Webinar

Deep Learning Summer School 2016 Videos

Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.

ACM Townhall Webinar with Peter Norvig

The Association for Computing Machinery is hosting a Town Hall with Peter Norvig on A.I., Machine Learning, and More. Peter Norvig is the Research Director at Google, and a leader in the field of Artificial Intelligence (AI). Topics he might discuss:

  • deep learning
  • future of AI
  • teaching AI
  • academia vs industry
  • advice for grad students

The free webinar is Thursday, December 08, 2016 at noon Eastern time.

Recent Free Online Books for Data Science

This is just a short list of a few books that I have have recently discovered online.

  • Model-Based Machine Learning – Chapters of this book become available as they are being written. It introduces machine learning via case studies instead of just focusing on the algorithms.
  • Foundations of Data Science – This is a much more academic-focused book which could be used at the undergraduate or graduate level. It covers many of the topics one would expect: machine learning, streaming, clustering and more.
  • Deep Learning Book – This book was previously available only in HTML form and not complete. Now, it is free and downloadable.

Learn to Analyze Big Data with R – Free Course

R is a hugely popular language among data scientists and statisticians. One of the difficulties with open-source R is the memory constraint. All the data needs to be loaded into a data.frame. Microsoft solves this problem with the RevoScaleR package of the Microsoft R Server. Just launched this week is an EdX course on
Analyzing Big Data with Microsoft R Server.

According the syllabus:

Upon completion, you will know how to use R for big-data problems.

Full Disclosure: I work at Microsoft, and the course instructor, Seth Mottaghinejad, is one of my colleagues.

Our World In Data

Our World in Data is data visualization site for exploring the history of civilization. The site was created by Max Roser. Our World in Data contains tons of information about many aspects of people’s lives. It also includes numerous visuals (like the one below) which can be easily shared or embedded on other sites.

Beware, the site is addicting, and you might spend a lot of time exploring data.

Google Announces Public Data Sets

Somewhat lost in the hype of Google’s Cloud Machine Learning announcement (which is itself neat), was the release of Google’s Public Data Sets.

I think this has been previously happening, but now Google has an official location for these public data sets stored in BigQuery. You can:

  • Access and use the data in your applications
  • Request Google to host your own public data set

It will be fun to watch this site expand with more public datasets. Happy Exploration!

Understanding Machine Learning: From Theory to Algorithms (Free Book Download)

Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Associate Professor at the School of Computer
Science and Engineering at The Hebrew University, Israel, and
Shai Ben-David, Professor in the School of Computer Science at the
University of Waterloo, Canada. The book looks very thorough. Below is just a sampling of the topics covered.

  • Bias-Complexity Tradeoff
  • Model Selection
  • Support Vector Machines
  • Decision Trees
  • Neural Networks
  • Clustering
  • Dimensionality Reduction
  • Feature Selection and Generation
  • Advanced Theory
  • And LOTS LOTS more….

Happy Learning!