Online or onsite, instructor-led live Random Forest training courses demonstrate through interactive hands-on practice how to use Random Forest to build machine learning algorithms for large datasets.
Random Forest 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 Random Forest trainings in the UK can be carried out locally on customer premises or in NobleProg corporate training centres.
NobleProg -- Your Local Training Provider
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course: Data Mining & Machine Learning with R
I genuinely liked excercises
L M ERICSSON LIMITED
Course: Machine Learning
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
The Jupyter notebook form, in which the training material is available
L M ERICSSON LIMITED
Course: Machine Learning
There were many exercises and interesting topics.
L M ERICSSON LIMITED
Course: Machine Learning
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
L M ERICSSON LIMITED
Course: Machine Learning
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
Course: Machine Learning
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
convolution filter
Francesco Ferrara - Inpeco SpA
Course: Introduction to Machine Learning
Machine learning, python, data manipulation
Siphelo Mapolisa - University Of South Africa
Course: Data Science: Analysis and Presentation
The theoretical explanations
Molatelo Tloubatla - University Of South Africa
Course: Data Science: Analysis and Presentation
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Course: Machine Learning Concepts for Entrepreneurs and Managers
The trainer very easily explained difficult and advanced topics.
Leszek K
Course: Artificial Intelligence Overview
Machine Translated
All like it
蒙 李
Course: Machine Learning Fundamentals with Python
Machine Translated
Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Alternation theory / practice effective!
CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
Progressive presentation and application of methods
Aurélien Briffaz - CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
Availability and adaptability, answers to questions
Jean-Michel MEOT - CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
Course: Octave nie tylko dla programistów
Machine Translated
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Remote Online Random Forest Course Events
Machine Learning with Random Forest
2022-03-02 09:30:00
14 hours
Random Forest Course Outlines Online
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in the UK (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.
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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