Our Data Science Friday webinars launched today, providing a good starting point for further exploration of machine learning topics. Today’s topic was “Getting Your Data Ready for Automated Machine Learning,” and you can access the session recording by signing up for the webinar series. Next Friday we’ll cover “Machine Learning and Statistics: Better Together” with @Jon McLoone, and we’ll dive deeper into machine learning functionality at upcoming sessions. Thanks to @Abrita Chakravarty for today’s presentation. Post any follow-up questions or comments!
REGISTER HERE:Webinar: Data Science Friday Webinars by Wolfram Research About This Webinar Series: Join us each Friday to learn about leveraging the power of the Wolfram Language for data science. In the first collection of this multipart series, we will focus on the wide range of state-of-the-art integrated machine learning capabilities available in the Wolfram Language. We’ll start with the first steps of importing data from many different sources and getting it ready for machine learning. Then we will look at highly automated functions like Predict, Classify, AnomalyDetection and FeatureExtraction, as well as the powerful symbolic Wolfram Neural Net Framework. The series will also include pointers to freely available repositories of neural net models and curated computable datasets, so you can jump right in and start your data science explorations. Each webinar is led by a Wolfram certified instructor and content expert who will cover weekly topics, poll the audience about the addition of new topics for the series and answer your questions.
Hi. @AbritaChakravarty
I registered for the seminar. I already saw the first video that has the name
Getting Your Data Ready for Automated Machine Learning.
Hello, @Luis Antonio Gonzalez, I see that you signed up yesterday and that you missed the notebook link. I will send that to you by email. I also encourage you to register for the full series, so that you receive reminders and emails with links to all the upcoming topics. Sign up for the Data Science Friday webinar series here.
The “Machine Learning and Statistics: Better Together Talk” by @Jon McLoone on Friday April 1 at 1:00 PM CT had many technical errors during the presentation. It would be nice to get a cleaned up version via email to watch later. In other words, please fix the audio and video by re-recording the webinar if you don’t mind. I don’t mind you extending the webinar next week by 30 minutes, but in the release of the webinar for later viewing, please take the time to fix the presentation by elimination of the technical problems from the video and replace these sections with the material that the presenter meant to present. Thanks!
We did run into some issues with the presenter’s internet connection towards the end of the session. We are working on preparing a recording of the presentation (without the audio dropout and screenshare issues) to share with our attendees.
I signed up for the Friday sessions, but I am busy during the day and have been watching the published videos as they become available. I would like to have access to the Mathematica notebooks, but I can’t find any link to them on-line. Can you please send me a link to the notebooks for the first two sessions? Thank you very much.
@Charles Glover and @John Snyder we will send out an event reminder email with the link to the download folder containing all the notebooks from the entire webinar series.
Sorry for the confusion about where you can download presentation notebooks. We will include the link in all upcoming reminders, so it’s easy to find. A reminder will be sent today, so please watch for that in your inbox. All the notebooks for the full series will be placed in a single location, so you can download the latest each week.
We want to be sure everyone who is interested is signed up for the series, so that you receive all the details you need. You will receive reminder emails, recording notifications and further resources. Sign up for Data Science Friday Webinars.
Looking forward to the next session in the Data Science Friday series: “Supervised and Unsupervised Learning” on Friday, April 8 at 1:00 PM Central Time (US & Canada) (GMT -5:00)
Just want to let everyone know that we’ve posted an improved and complete recording of @Jon McLoone’s presentation from last week. To access recordings, go to the series page, scroll down to find the title you want to see, and click on “Start this section.” The video is embedded on each webinar page. Abrita also plans to revisit this final section of Jon’s presentation at the end of this week’s webinar. Hope to see you there!
No you do not need to check the data distribution beforehand and there is no requirement for the data to be from a normal distribution when you use a functions like Classify or Predict. The automated machine learning functions are capable of handling any type of data from any distribution. You do have the option of specifying a FeatureExtractor with Classify (or Predict), if you have some specific thoughts about preprocessing the input features. Else Classify (or Predict) will automatically choose a suitable feature extractor based on the data.
LinearModelFit is specific in that it can only fit a linear regression model and can only work with numeric data. Classify or Predict can work on any type of data (numbers, text, images etc.) and has more options available in terms of the type of algorithm you want to use (DecisionTree, NaiveBayes, etc.)
A methodological question on using Predict versus Classify.
I am working on a model to forecast attendance in a non-contractual setting, which effectively means that all the data I have is in the historic attendance.
The attendance per participant can be recorded in “binary” format so for example the first event the attendance was 1 and the second 0 so we can create list with attendance per participants as so {{1,0},{2,0},{3,1}..}.
As far as I can see, I can use Predict to forecast each individual’s attendance series, or I can use Classify. If I use the latter, I can treat each event as an “attended” (value 1) versus “unattended” (value 0) class.
Are both methods equally applicable for this case or is there reason why I should use one and not the other?
