Genetic Algorithms

  28 hours

AI in business and Society & The future of AI - AI/Robotics

  7 hours

Intelligent Testing

  14 hours

Artificial Intelligence (AI) for Managers

  7 hours

TensorFlow Lite for Android

  21 hours

TensorFlow Lite for iOS

  21 hours

TensorFlow Lite for Embedded Linux

  21 hours

Tensorflow Lite for Microcontrollers

  21 hours

OptaPlanner in Practice

  21 hours

UiPath for Intelligent Process Automation (IPA)

  14 hours

Introduction to Data Science and AI using Python

  35 hours

Big Data Business Intelligence for Telecom and Communication Service Providers

  35 hours

AI in Digital Marketing

  7 hours

AI-100: Designing & Implementing Azure AI Solutions- AI-100T01-A

  28 hours

AI-900T00: Microsoft Azure AI Fundamentals

  7 hours

Azure Machine Learning

  14 hours

MLOps for Azure Machine Learning

  14 hours

Azure Machine Learning (AML)

  21 hours

IBM Cloud Pak for Data

  14 hours

NLP: Natural Language Processing with R

  21 hours

Embedding Projector: Visualizing Your Training Data

  14 hours

Artificial Intelligence (AI) for Robotics

  21 hours

AI and Robotics for Nuclear

  80 hours

AI and Robotics for Nuclear - Extended

  120 hours

Natural Language Processing - AI/Robotics

  21 hours

Machine Learning for Robotics

  21 hours

AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

  28 hours

From Data to Decision with Big Data and Predictive Analytics

  21 hours

Big Data Business Intelligence for Criminal Intelligence Analysis

  35 hours

Matlab for Predictive Analytics

  21 hours

Predictive Modelling with R

  14 hours

Introduction to R with Time Series Analysis

  21 hours

Visual Analytics – Data science

  14 hours

Machine Learning and Big Data

  7 hours

From Zero to AI

  35 hours

Machine Learning for Banking (with R)

  28 hours

Machine Learning for Banking (with Python)

  21 hours

Machine Learning for Finance (with Python)

  21 hours

Machine Learning for Finance (with R)

  28 hours

Machine Learning on iOS

  14 hours

Artificial Intelligence Overview

  7 hours

Applied Machine Learning

  14 hours

Deep Learning for Finance (with R)

  28 hours

Deep Learning for Banking (with Python)

  28 hours

Deep Learning for Banking (with R)

  28 hours

Deep Learning for Finance (with Python)

  28 hours

Matlab for Deep Learning

  14 hours

Artificial Neural Networks, Machine Learning, Deep Thinking

  21 hours

Introduction Deep Learning and Neural Network for Engineers

  21 hours

Microsoft Cognitive Toolkit 2.x

  21 hours

Deep Reinforcement Learning with Python

  21 hours

Reinforcement Learning with Java

  21 hours

Fundamentals of Reinforcement Learning

  21 hours

OpenAI Gym

  7 hours

Torch for Machine and Deep Learning

  21 hours

Octave not only for programmers

  21 hours

Introduction to the use of neural networks

  7 hours

Neural Network in R

  14 hours

Artificial Intelligence in Automotive

  14 hours

Neural Networks Fundamentals using TensorFlow as Example

  28 hours

TPU Programming: Building Neural Network Applications on Tensor Processing Units

  7 hours

Understanding Deep Neural Networks

  35 hours

Applied AI from Scratch

  28 hours

Natural Language Processing with TensorFlow

  35 hours

Deep Learning for NLP (Natural Language Processing)

  28 hours

Fraud Detection with Python and TensorFlow

  14 hours

Deep Learning for Vision

  21 hours

Deep Learning with TensorFlow

  21 hours

Pattern Recognition

  21 hours

PaddlePaddle

  21 hours

Pattern Matching

  14 hours

Fiji: Introduction to Scientific Image Processing

  21 hours

Artificial Intelligence for Mechatronics

  21 hours

Deep Learning Neural Networks with Chainer

  14 hours

Natural Language Processing

  21 hours

Natural Language Processing with Python

  28 hours

Natural Language Processing with Deep Dive in Python and NLTK

  35 hours

NLP with Deeplearning4j

  14 hours

Mastering Deeplearning4j

  21 hours

DeepLearning4J for Image Recognition

  21 hours

Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

  21 hours

Python for Natural Language Generation

  21 hours

Natural Language Processing (NLP) with Python spaCy

  14 hours

Building Chatbots in Python

  21 hours

Computer Vision with Python

  14 hours

Deep Learning for Vision with Caffe

  21 hours

Computer Vision with SimpleCV

  14 hours

Real-Time Object Detection with YOLO

  7 hours

Deep Learning for Telecom (with Python)

  28 hours

AI Awareness for Telecom

  14 hours

Advanced Deep Learning with Keras and Python

  14 hours

Deep Learning for Self Driving Cars

  21 hours

Deep Learning with Keras

  21 hours

Python and Deep Learning with OpenCV 4

  14 hours

Computer Vision with OpenCV

  28 hours

ParlAI for Conversational AI

  14 hours

Chatbots for Developers

  14 hours

NLP with Python and TextBlob

  14 hours

Scilab

  14 hours

Data Mining with Weka

  14 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

DataRobot

  7 hours

Machine Learning Fundamentals with Scala and Apache Spark

  14 hours

Machine Learning with PredictionIO

  21 hours

Turning Data into Intelligent Action with Cortana Intelligence

  28 hours

Kubeflow

  35 hours

Kubeflow Fundamentals

  28 hours

Kubeflow on OpenShift

  28 hours

Core ML for iOS App Development

  14 hours

Mathematica for Machine Learning

  14 hours

Machine Learning with Python and Pandas

  14 hours

MLOps: CI/CD for Machine Learning

  35 hours

MLflow

  21 hours

Machine Learning and AI with ML.NET

  21 hours

Amazon Web Services (AWS) SageMaker

  21 hours

Practical Quantum Computing

  10 hours

Quantum Computing with IBM Quantum Experience

  14 hours

Fundamentals of Quantum Computing and Quantum Physics

  21 hours

Machine Learning Algorithms in Julia

  21 hours

Scaling Data Pipelines with Spark NLP

  14 hours

Insurtech: A Practical Introduction for Managers

  14 hours

OpenNN: Implementing Neural Networks

  14 hours

OpenNMT: Setting Up a Neural Machine Translation System

  7 hours

Facebook NMT: Setting up a Neural Machine Translation System

  7 hours

OpenFace: Creating Facial Recognition Systems

  14 hours

Hardware-Accelerated Video Analytics

  14 hours

Using Computer Network ToolKit (CNTK)

  28 hours

Apache SystemML for Machine Learning

  14 hours

Raspberry Pi + OpenCV for Facial Recognition

  21 hours

Mastering Apache SINGA

  21 hours

Marvin Framework for Image and Video Processing

  14 hours

Sphinx: Developing Speech-Enabled Applications

  21 hours

Cognitive Computing: An Introduction for Business Managers

  7 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

AutoML with Auto-Keras

  14 hours

AutoML with Auto-sklearn

  14 hours

H2O AutoML

  14 hours

Quantum Computing with Cirq Framework

  21 hours

Getting Started with Quantum Computing and Q#

  14 hours

Microsoft Bot Framework Fundamentals

  14 hours

Microsoft Bot Framework Composer

  14 hours

Building Deep Learning Models with Apache MXNet

  21 hours

Accelerating Deep Learning with FPGA and OpenVINO

  35 hours

Distributed Deep Learning with Horovod

  7 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Machine Learning with Random Forest

  14 hours

AdaBoost Python for Machine Learning

  14 hours

AlphaFold

  7 hours

He was very informative and helpful.

Pratheep Ravy [Predictive Modelling with R]

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]

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 [Introduction to the use of neural networks]

The interactive part, tailored to our specific needs.

Thomas Stocker [Introduction to the use of neural networks]

I did like the exercises.

Office for National Statistics [Natural Language Processing with Python]

I genuinely enjoyed the hands-on approach.

Kevin De Cuyper [Computer Vision with OpenCV]

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

Mohamed Salama [Data Mining & Machine Learning with R]

The topic is very interesting.

Wojciech Baranowski [Introduction to Deep Learning]

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.

Grzegorz Mianowski [Introduction to Deep Learning]

Topic. Very interesting!.

Piotr [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. [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 [Introduction to Deep Learning]

The global overview of deep learning.

Bruno Charbonnier [Advanced Deep Learning]

The exercises are sufficiently practical and do not need high knowledge in Python to be done.

Alexandre GIRARD [Advanced Deep Learning]

Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.

Paul Kassis [Advanced Deep Learning]

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus [Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple]

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara [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 [Neural Networks Fundamentals using TensorFlow as Example]

Very good all round overview. Good background into why Tensorflow operates as it does.

Kieran Conboy [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 [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 [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 [Machine Learning and Deep Learning]

I really enjoyed the coverage and depth of topics.

Anirban Basu [Machine Learning and Deep Learning]

I liked the new insights in deep machine learning.

Josip Arneric [Neural Network in R]

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Tea Poklepovic [Neural Network in R]

I mostly enjoyed the graphs in R :))).

Faculty of Economics and Business Zagreb [Neural Network in R]

The deep knowledge of the trainer about the topic.

Sebastian Görg [Introduction to Deep Learning]

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee [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 [Neural Networks Fundamentals using TensorFlow as Example]

I was benefit from topic selection. Style of training. Practice orientation.

Commerzbank AG [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 [Python for Advanced Machine Learning]

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn [Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP]

Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.

Jamie Martin-Royle - NBrown Group [From Data to Decision with Big Data and Predictive Analytics]

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan Mistry - NBrown Group [From Data to Decision with Big Data and Predictive Analytics]

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]

The easy use of the VideoCapture functionality to acquire video images from laptop camera.

- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]

I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.

- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]

I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.

- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]

It was easy to follow.

- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]

the matter was well presented and in an orderly manner.

Marylin Houle - Ivanhoe Cambridge [Introduction to R with Time Series Analysis]

I was benefit from the passion to teach and focusing on making thing sensible.

Zaher Sharifi - GOSI [Advanced Deep Learning]

About face area.

- 中移物联网 [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 [Deep Reinforcement Learning with Python]

This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.

  [Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP]

It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.

  [Insurtech: A Practical Introduction for Managers]





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