This instructor-led, live training in hong-kong (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
This instructor-led, live training in hong-kong (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.
By the end of this training, participants will:
Understand the evolution and trends for machine learning.
Know how machine learning is being used across different industries.
Become familiar with the tools, skills and services available to implement machine learning within an organization.
Understand how machine learning can be used to enhance data mining and analysis.
Learn what a data middle backend is, and how it is being used by businesses.
Understand the role that big data and intelligent applications are playing across industries.
RapidMiner 是一個開源數據科學軟體平臺,用於快速應用程式原型設計和開發。它包括用於數據準備、機器學習、深度學習、文本挖掘和預測分析的集成環境。
在這個由講師指導的實時培訓中,參與者將學習如何使用 RapidMiner Studio 進行數據準備、機器學習和預測模型部署。
在本次培訓結束時,參與者將能夠:
安裝與設定RapidMiner
使用 RapidMiner 準備和可視化數據
驗證機器學習模型
混搭數據並創建預測模型
在業務流程中實施預測分析
故障排除和優化 RapidMiner
觀眾
數據科學家
工程師
開發人員
課程形式
部分講座、部分討論、練習和大量動手實踐
注意
要申請本課程的定製培訓,請聯繫我們進行安排。
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客戶評論 (25)
亨特很棒,非常有吸引力,知識淵博,風度翩翩。 做得很好。
Rick Johnson - Laramie County Community College
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機器翻譯
VM 是個好主意
Vincent - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
機器翻譯
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - TCMT
Course - Machine Learning with Python – 2 Days
The clarity with which it was presented
John McLemore - Motorola Solutions
Course - Deep Learning for Telecom (with Python)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rekas - Bitcomp Sp. z o.o.
Course - Machine Learning on iOS
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Course - Machine Learning with Python – 4 Days
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 TM
Course - Kubeflow
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
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The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.
Robert Baker
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Convolution filter
Francesco Ferrara
Course - Introduction to Machine Learning
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
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Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
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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
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 liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Course - Machine Learning
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course - Python for Advanced Machine Learning
Very flexible.
Frank Ueltzhoffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.
Sebastiaan Holman
Course - Machine Learning and Deep Learning
The global overview of deep learning.
Bruno Charbonnier
Course - Advanced Deep Learning
The topic is very interesting.
Wojciech Baranowski
Course - Introduction to Deep Learning
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
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