Course Outline
Introduction
- Overview of Random Forest features and advantages
- Understanding decision trees and ensemble methods
Getting Started
- Setting up the libraries (Numpy, Pandas, Matplotlib, etc.)
- Classification and regression in Random Forests
- Use cases and examples
Implementing Random Forest
- Preparing data sets for training
- Training the machine learning model
- Evaluating and improving accuracy
Tuning the Hyperparameters in Random Forest
- Performing cross-validations
- Random search and Grid search
- Visualizing training model performance
- Optimizing hyperparameters
Best Practices and Troubleshooting Tips
Summary and Next Steps
Requirements
- An understanding of machine learning concepts
- Python programming experience
Audience
- Data scientists
- Software engineers
Testimonials
I like that it focuses more on the how-to of the different text summarization methods
Text Summarization with Python Course
Going through the notebooks, becoming more familiar with Qiskit and the various ways to do things.
Bank of Canada
Practical Quantum Computing Course
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
MLflow Course
Very very competent trainer who know how to adapt to his audience, and to solve problems Interactive presentation
OLEA MEDICAL
MLflow Course
The Exercises
Khaled Altawallbeh - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Interactive, a lot of exercises
Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
The details and the presentation style.
Cristian Mititean - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kubeflow Course
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Kubeflow Course
Wiedza i umiejetnosc jej przekazania
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Humor prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Bardzo merytoryczne szkolenie, bardzo duża wiedza prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Applied AI from Scratch in Python Course
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
I like that it focuses more on the how-to of the different text summarization methods
Text Summarization with Python Course
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Sebastian Pawłowski, Bitcomp Sp. z o.o.
Machine Learning on iOS Course
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
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
Machine Learning Concepts for Entrepreneurs and Managers Course
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Data Mining & Machine Learning with R Course
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Machine Learning Course
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
Machine Learning Course
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Machine Learning Course
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
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Artificial Neural Networks, Machine Learning, Deep Thinking Course
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
Convolution filter