Online or onsite, instructor-led live Mathematica training courses in Salvador.
Salvador Suarez Trade
Av. Tancredo Neves, 450 - Caminho das Árvores, Salvador, Brazil, 41819-900
The Salvador Suarez Trade Center office space is situated on the 16th floor of a 34-story granite blue glass skyscraper, loca...
The Salvador Suarez Trade Center office space is situated on the 16th floor of a 34-story granite blue glass skyscraper, located on a main road through the financial district. Salvador is the second most popular tourist destination in Brazil. National and international real estate developers are investing in the expansion of the city, and construction is one of the most important commercial activities. The city is also attracting major global companies, creating oil plants, car factories and chemicals near Salvador. The port plays a key role in local trade, especially for the transport of agricultural products from the surrounding region. The city is popular for conferences. Salvador Suarez Trade Center has a fully equipped auditorium, a lan house and its own parking lot, and is near one of the city's main shopping centers. You can easily reach the international airport and other transport links.
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 Salvador (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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