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 training can be carried out locally on customer premises in Malaga or in NobleProg corporate training centers in Malaga.
NobleProg -- Your Local Training Provider
Malaga City Center
NobleProg Málaga, Street Cortina del Muelle, 1, Malaga, spain, 29015
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan ...
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan city that loves citizens from all over the world. Located in the south of the Iberian Peninsula, it is characterized by having multiple geographical sectors, such as Its abundant beaches or its high mountains.
The airport is connected to the city center and with the Málaga-María Zambrano Station by the Cercanías railway line and by the A Express of the EMT, which also has a stop inside the Bus Station, where you can link to the destination To other Spanish cities and the Costa del Sol.
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 Malaga (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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