Online or onsite, instructor-led live Ensemble Learning training courses demonstrate through interactive hands-on practice how to use ensemble machine learning techniques to combine multiple models for improved prediction accuracy and robustness.
Ensemble Learning 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 Ensemble Learning trainings in Tokyo can be carried out locally on customer premises or in NobleProg corporate training centers.
Ensemble Learning is also known as Ensemble Methods or Ensemble Modeling.
NobleProg -- Your Local Training Provider
Tokyo Shinjuku Park Tower
Shinjuku Park Tower, 3 Chome-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, japan, 160-0023
The Shinjuku Park Tower Centre is on the 30th floor of a 52-storey landmark building in the business district of Shinjuku - a major commercial and government administrative area.
Nishi Shinjuku is noted for its impressive skyline of skyscrapers. The centre is three minutes' walk from one of the tallest buildings in Japan - the Tokyo Metropolitan Government Building, designed by the same architect who created the Park Tower. The business community knows the building for its five-star hotel, which is above the centre. Many corporations have their HQs here, including airlines, food companies, technology giants and computer gaming firms. The centre has a meeting room that will take up to 60 people. There's a complimentary shuttle bus service from Shinjuku station (the busiest station in the world) to the building and excellent public transport links to Narita International Airport by airport limousine from Shinjuku Station. It's next to the Expressway Number 4 on the motorway network.
Tokyo, Shibuya Glass City
35th Floor, Glass City Shibuya 6F, 16-28 , Tokyo, japan, 150-0036
The Shibuya Glass City Business centre is located on the 6th floor of this 11 storey grade A office building in Tokyo's colourful Shibuya business district. This is also known as Tokyo's silicon valley and is home to many IT and technology businesses. Conveniently located, the nearest station is Shinsen on the Keio Inokashira line and the centre is an 8 minute walk from Shibuya JR station which offers connections to JR East Japan Rail, Keio Rail, Tokyu Rail and the Tokyo Metro (Fukutoshin , Ginza, Hanzomon & Den'entoshi lines). There is convenient access to all major cities and both Haneda and Narita international airports. The area has many major department stores, shopping malls, restaurants and night clubs and Yoyogi Park is nearby. With its central location and excellent services, Regus Shibuya Glass City Centre is a flexible and convenient venue in the middle of this major business district.
This instructor-led, live training in Tokyo (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques.
By the end of this training, participants will be able to:
Install and configure LightGBM.
Understand the theory behind gradient boosting and decision tree algorithms
Use LightGBM for basic and advanced machine learning tasks.
Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
Integrate LightGBM with other machine learning frameworks.
This instructor-led, live training in Tokyo (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building machine learning models with AdaBoost.
Understand the ensemble learning approach and how to implement adaptive boosting.
Learn how to build AdaBoost models to boost machine learning algorithms in Python.
Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.
This instructor-led, live training in Tokyo (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building machine learning models with Random forest.
Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
Evaluate and optimize machine learning model performance by tuning the hyperparameters.
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