Online or onsite, instructor-led live Wolfram training courses show participants how to program in Wolfram through interactive hands-on practice.
Wolfram training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Wolfram trainings in Montpellier can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Montpellier, Garosud
NobleProg Montpellier, 48, rue Claude Balbastre, Montpellier, france, 34070
– Access Tram stop Victoire 2 Line 2 direction Saint Jean de Védas.
-Tram access then Bus take tram line 2 Sabine stop to...
– Access Tram stop Victoire 2 Line 2 direction Saint Jean de Védas.
-Tram access then Bus take tram line 2 Sabine stop towards Saint Jean de Védas then the “La Ronde” bus stop Patrice Lumumba.
- Access by car from the A9 exit 31 “Saint Jean de Védas” Montpellier Ouest, silver cross MOSSON.
-Access by train from Saint Roch station, Tram line 2 towards Saint Jean de Védas, Victory 2 stop.
– Access by plane from Montpellier airport 10 minutes by car, exit 31 of the A9 towards Barcelona or by bus from the airport line 120 to Place de l'Europe then Tram line 2 towards Saint Jean de Védas stop Victory 2
The "Mathematica - wprowadzenie, wizualizacja i prezentacja danych" training is aimed at participants with basic mathematical knowledge. Mathematica, as a powerful mathematical engine, enables effective calculations, creating text documents, building charts and presenting data. Participants will gain practical skills in working with Mathematica, used, among others, in mathematics, physics, biology, financial analysis and other fields.
This instructor-led, live training in Montpellier (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis.
By the end of this training, participants will be able to:
Build and train machine learning models.
Import and prepare data for machine learning.
Separate training data from test data.
Explore deep learning and neural network applications in data analysis.
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