Artificial Intelligence for City Planning

  14 hours

Artificial Intelligence Overview

  7 hours

From Zero to AI

  35 hours

Algebra for Machine Learning

  14 hours

Applied AI from Scratch in Python

  28 hours

Applied Machine Learning

  14 hours

Machine Learning

  21 hours

Dataiku for Enterprise AI and Machine Learning

  21 hours

DeepMind Lab

  14 hours

Data Mining & Machine Learning with R

  14 hours

Encog: Advanced Machine Learning

  14 hours

Encog: Introduction to Machine Learning

  14 hours

Feature Engineering for Machine Learning

  14 hours

Fundamentals of Artificial Intelligence and Machine Learning

  28 hours

GANs and Variational Autoencoders in Python

  14 hours

Machine Learning with Python - Micro Learning

  4 hours

Machine Learning for Banking (with Python)

  21 hours

Machine Learning for Banking (with R)

  28 hours

Machine Learning and Big Data

  7 hours

Machine Learning Concepts for Entrepreneurs and Managers

  21 hours

Machine Learning for Finance (with Python)

  21 hours

Machine Learning for Finance (with R)

  28 hours

Machine Learning with Python – 2 Days

  14 hours

Machine Learning Fundamentals with R

  14 hours

Introduction to Machine Learning

  7 hours

Machine Learning on iOS

  14 hours

Machine Learning in business – AI/Robotics

  14 hours

Machine Learning with Python – 4 Days

  28 hours

Machine Learning – Data science

  21 hours

OpenNLP for Text Based Machine Learning

  14 hours

Python: Machine Learning with Text

  21 hours

Recommender Systems with Python

  14 hours

Snorkel: Rapidly Process Training Data

  7 hours

Text Summarization with Python

  14 hours

Vertex AI

  7 hours

XGBoost for Gradient Boosting

  14 hours

AI Awareness for Telecom

  14 hours

Azure Machine Learning (AML)

  21 hours

Azure Machine Learning

  14 hours

MLOps for Azure Machine Learning

  14 hours

Artificial Neural Networks, Machine Learning, Deep Thinking

  21 hours

Applied AI from Scratch

  28 hours

Amazon Web Services (AWS) SageMaker

  21 hours

Core ML for iOS App Development

  14 hours

Turning Data into Intelligent Action with Cortana Intelligence

  28 hours

Kubeflow

  35 hours

Kubeflow Fundamentals

  28 hours

Mathematica for Machine Learning

  14 hours

Artificial Intelligence for Mechatronics

  21 hours

Machine Learning and AI with ML.NET

  21 hours

MLflow

  21 hours

Machine Learning Fundamentals with Scala and Apache Spark

  14 hours

MLOps: CI/CD for Machine Learning

  35 hours

Machine Learning for Robotics

  21 hours

Octave not only for programmers

  21 hours

Machine Learning with PredictionIO

  21 hours

Machine Learning with Python and Pandas

  14 hours

Practical Quantum Computing

  10 hours

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]

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama [Data Mining & Machine Learning with R]

I genuinely liked excercises

- L M ERICSSON LIMITED [Machine Learning]

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED [Machine Learning]

The Jupyter notebook form, in which the training material is available

- L M ERICSSON LIMITED [Machine Learning]

There were many exercises and interesting topics.

- L M ERICSSON LIMITED [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 [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 [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 [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 [Machine Learning – Data science]

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]

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 [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 [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 [Machine Learning – Data science]

Adjusting to our needs

Sumitomo Mitsui Finance and Leasing Company, Limited [Kubeflow]

convolution filter

Francesco Ferrara - Inpeco SpA [Introduction to Machine Learning]

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]

The trainer very easily explained difficult and advanced topics.

Leszek K [Artificial Intelligence Overview]

All like it

蒙 李 [Machine Learning Fundamentals with Python]

Communication with lecturers

文欣 张 [Artificial Neural Networks, Machine Learning, Deep Thinking]

like it all

lisa xie [Artificial Neural Networks, Machine Learning, Deep Thinking]

Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.

- Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu [Octave nie tylko dla programistów]

I like that it focuses more on the how-to of the different text summarization methods

  [Text Summarization with Python]





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