
Online or onsite, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine 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. The US onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
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
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course: Data Mining & Machine Learning with R
The topic is very interesting.
Wojciech Baranowski
Course: Introduction to Deep Learning
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
Grzegorz Mianowski
Course: Introduction to Deep Learning
Topic. Very interesting!.
Piotr
Course: Introduction to Deep Learning
Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.
Dolby Poland Sp. z o.o.
Course: Introduction to Deep Learning
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek
Course: Introduction to Deep Learning
The global overview of deep learning.
Bruno Charbonnier
Course: Advanced Deep Learning
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course: Neural Networks Fundamentals using TensorFlow as Example
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 training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course: Machine Learning and Deep Learning
I really enjoyed the coverage and depth of topics.
Anirban Basu
Course: Machine Learning and Deep Learning
The deep knowledge of the trainer about the topic.
Sebastian Görg
Course: Introduction to Deep Learning
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course: TensorFlow for Image Recognition
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
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
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with 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 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 surprized).
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
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (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
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
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
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
the way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The excersise where we should train a network to approximate a function
Nercia Utbildning AB
Course: Deep Learning with TensorFlow 2.0
having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine Learning
The trainers knowledge of the topics he was teaching.
Premier Partnership
Course: Python for Advanced Machine Learning
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Machine Learning Subcategories
ML (Machine Learning) Course Outlines
- Understand the key concepts and principles behind Generative Pre-trained Transformers.
- Comprehend the architecture and training process of GPT models.
- Utilize GPT-3 for tasks such as text generation, completion, and translation.
- Explore the latest advancements in GPT-4 and its potential applications.
- Apply GPT models to their own NLP projects and tasks.
- 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.
- Troubleshoot common issues in LightGBM.
- Understand advanced deep learning architectures and techniques for text-to-image generation.
- Implement complex models and optimizations for high-quality image synthesis.
- Optimize performance and scalability for large datasets and complex models.
- Tune hyperparameters for better model performance and generalization.
- Integrate Stable Diffusion with other deep learning frameworks and tools.
- Understand how Vertex AI works and use it as a machine learning platform.
- Learn about machine learning and NLP concepts.
- Know how to train and deploy machine learning models using Vertex AI.
- Understand the principles of distributed deep learning.
- Install and configure DeepSpeed.
- Scale deep learning models on distributed hardware using DeepSpeed.
- Implement and experiment with DeepSpeed features for optimization and memory efficiency.
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
- Understand the principles of Stable Diffusion and how it works for image generation.
- Build and train Stable Diffusion models for image generation tasks.
- Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
- Optimize the performance and stability of Stable Diffusion models.
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
- Apply advanced Reinforcement Learning algorithms to solve real-world problems.
- Build a Deep Learning Agent.
- Understand the fundamental concepts of deep learning.
- Learn the applications and uses of deep learning in telecom.
- Use Python, Keras, and TensorFlow to create deep learning models for telecom.
- Build their own deep learning customer churn prediction model using Python.
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
Last Updated: