Online or onsite, instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems.
AI 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 UAE onsite live Artificial Intelligence (AI) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
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
Course: Neural Network in R
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course: Neural Network in R
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
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.
Course: 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
Course: 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
Course: From Data to Decision with Big Data and Predictive Analytics
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
The easy use of the VideoCapture functionality to acquire video images from laptop camera.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: 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
Course: 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
Course: Computer Vision with OpenCV
It was easy to follow.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course: Introduction to R with Time Series Analysis
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
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.
Course: Insurtech: A Practical Introduction for Managers
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
The trainer's patience
European Space Agency (ESA/ESTEC)
Course: Getting Started with Quantum Computing and Q#
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 surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
the last day. generation part
Accenture Inc
Course: Python for Natural Language Generation
The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.
Accenture Inc
Course: Python for Natural Language Generation
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The remote classroom setting worked very well
Trimac Management Services LP
Course: Introduction to R with Time Series Analysis
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
Course: Introduction to R with Time Series Analysis
The many practical examples / assignments that we went through were great. For me, I learn better by seeing examples and applying them elsewhere. The use of real data and applying what was taught against it was extremely valuable. Michaels PowerPoint presentations and his ability to work through each solution was invaluable.
Trimac Management Services LP
Course: Introduction to R with Time Series Analysis
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
The exercises.
Elena Velkova - CEED Bulgaria
Course: Predictive Modelling with R
Practical exercises with R were very helpful.
CEED Bulgaria
Course: Predictive Modelling with R
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
Quantum computing algorithms and related theoretical background know-how of the trainer is excellent. Especially I'd like to emphasize his ability to detect exactly when I was struggling with the material presented and he provided time&support for me to really understand the topic - that was great and very beneficial! Virtual setup with Zoom worked out very well, as well as arrangements regarding training sessions and breaks sequences. It was a lot of material/theory to cover in "only" 2 days, wo the trainer had nicely adjusted the amount according to the progress related to my understanding of the topics. Maybe planning 3 days for absolute beginners would be better to cover all the material and content outlined in the agenda. I very much liked the flexibility of the trainer to answer my specific questions to the training topics, even additionally coming back after the breaks with more explanation in case neccessary. Big thank you again for the sessions! Well done!
Giorgi Ediberidze
Course: Quantum Computing with IBM Quantum Experience
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.
Course: 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.
Course: Insurtech: A Practical Introduction for Managers
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Artificial Intelligence Subcategories in the UAE
AI (Artificial Intelligence) Course Outlines in the UAE
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Overview
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
7 hours
Overview
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
21 hours
Overview
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
14 hours
Overview
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
21 hours
Overview
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
28 hours
Overview
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
14 hours
Overview
This instructor-led, live training in the UAE (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
14 hours
Overview
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
21 hours
Overview
This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
21 hours
Overview
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
21 hours
Overview
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
21 hours
Overview
PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
14 hours
Overview
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
14 hours
Overview
This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists who wish to use Pandas to preform predictive analysis with machine learning.
By the end of this training, participants will be able to:
- Perform data wrangling in Python.
- Conduct ETL operations for machine learning.
- Create data visualizations with Pandas
By the end of this training, participants will be able to:
- Perform data wrangling in Python.
- Conduct ETL operations for machine learning.
- Create data visualizations with Pandas
21 hours
Overview
In this instructor-led, live training in the UAE, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- 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.
By the end of this training, participants will be able to:
- 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.
21 hours
Overview
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
21 hours
Overview
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app.
By the end of this training, participants will be able to:
- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
14 hours
Overview
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
14 hours
Overview
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
14 hours
Overview
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
7 hours
Overview
This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
14 hours
Overview
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
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