Online or onsite, instructor-led live Mathematica training courses in Lausanne.
Lausanne
Rue du Port-Franc 22, Lausanne, Switzerland, 1003
Overview
Our training facilities are located at Rue du Port-Franc 22 in Lausanne. Located on th...
Overview
Our training facilities are located at Rue du Port-Franc 22 in Lausanne. Located on the fourth floor of a well-kept office building, our premises offer enough space for successful training courses in the heart of Lausanne.
Directions
The NobleProg training facilities are located in the heart of downtown Lausanne, the centre of this vibrant city.
Parking
Cars can be parked in the area along Rue du Port-Franc and the nearby side streets, even if you may have to search for a moment. There is no charge for parking.
Local Amenities
Around the Rue du Port-Franc there are numerous small restaurants and shops where you can eat well and cheaply. There are also some hotels close by if you need accommodation for the training.
Introduces the core technologies needed to become an adept user of Mathematica, including the Wolfram Language, the notebook interface, programming fundamentals, visualization and interactivity features, and mathematics and statistics functionality. Small class sizes allow for interaction with the instructor, and in-person courses allow additional time for discussion, exercises, and Q&A.
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 Lausanne (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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