Local, 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 "onsite live training" or "remote live training". Onsite live Machine Learning trainings in Saudi Arabia can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Ref material to use later was very good.
PAUL BEALES- Seagate Technology.
Course: Applied Machine Learning
What did you like the most about the training?: Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away. This was an excellent course. One of the best I have had.
Matthew Thomas - British Telecom
Course: Applied Machine Learning
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
The subject. It seemed interesting, but I left knowing not much more than before.
Radoslaw Labedzki
Course: Introduction to Deep Learning
I liked that this course had very interesting subject.
Wojciech Wilk
Course: Introduction to Deep Learning
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Neural Networks Fundamentals using TensorFlow as Example
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
Very flexible.
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
I generally enjoyed the flexibility.
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
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
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
The trainer very easily explained difficult and advanced topics.
Leszek K
Course: Artificial Intelligence Overview
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All like it
蒙 李
Course: Machine Learning Fundamentals with Python
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way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
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Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
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Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
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like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
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Big and up-to-date knowledge of leading and practical application examples.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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work on colaborators,
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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A wide range of topics covered and substantial knowledge of the leaders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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| Code | Name | Duration | Overview |
|---|---|---|---|
| aiint | Artificial Intelligence Overview | 7 hours | This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development. |
| mlbankingr | Machine Learning for Banking (with R) | 28 hours | In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking 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 live projects. Audience - Developers - Data scientists - Banking professionals with a technical background Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. 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 implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use R to create deep learning models for finance - Build their own deep learning stock price prediction model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlfinancer | Machine Learning for Finance (with R) | 28 hours | 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 |
| mlfinancepython | Machine Learning for Finance (with Python) | 21 hours | 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 |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours | DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to: - Design and code DL for NLP using Python libraries. - Create Python code that reads a substantially huge collection of pictures and generates keywords. - Create Python Code that generates captions from the detected keywords. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| textsum | Text Summarization with Python | 14 hours | In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations. In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text. By the end of this training, participants will be able to: - Use a command-line tool that summarizes text. - Design and create Text Summarization code using Python libraries. - Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17 Audience - Developers - Data Scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| undnn | Understanding Deep Neural Networks | 35 hours | This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy. Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: - have a good understanding on deep neural networks(DNN), CNN and RNN - understand TensorFlow’s structure and deployment mechanisms - be able to carry out installation / production environment / architecture tasks and configuration - be able to assess code quality, perform debugging, monitoring - be able to implement advanced production like training models, building graphs and logging |
| opennlp | OpenNLP for Text Based Machine Learning | 14 hours | 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 |
| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 hours | 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 banking 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. Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| matlabdl | Matlab for Deep Learning | 14 hours | In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to: - Build a deep learning model - Automate data labeling - Work with models from Caffe and TensorFlow-Keras - Train data using multiple GPUs, the cloud, or clusters Audience - Developers - Engineers - Domain experts Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. 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 implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use R to create deep learning models for banking - Build their own deep learning credit risk model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| tensorflowserving | TensorFlow Serving | 7 hours | TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment. By the end of this training, participants will be able to: - Train, export and serve various TensorFlow models - Test and deploy algorithms using a single architecture and set of APIs - Extend TensorFlow Serving to serve other types of models beyond TensorFlow models Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlios | Machine Learning on iOS | 14 hours | 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 |
| pythontextml | Python: Machine Learning with Text | 21 hours | 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 |
| encogintro | Encog: Introduction to Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: - Prepare data for neural networks using the normalization process - Implement feed forward networks and propagation training methodologies - Implement classification and regression tasks - Model and train neural networks using Encog's GUI based workbench - Integrate neural network support into real-world applications Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| encogadv | Encog: Advanced Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: - Implement different neural networks optimization techniques to resolve underfitting and overfitting - Understand and choose from a number of neural network architectures - Implement supervised feed forward and feedback networks Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| radvml | Advanced Machine Learning with R | 21 hours | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: - Use techniques as hyper-parameter tuning and deep learning - Understand and implement unsupervised learning techniques - Put a model into production for use in a larger application Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonadvml | Python for Advanced Machine Learning | 21 hours | In this instructor-led, live training, 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. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| openface | OpenFace: Creating Facial Recognition Systems | 14 hours | 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 |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use Python, Keras, and TensorFlow to create deep learning models for banking - Build their own deep learning credit risk model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use Python, Keras, and TensorFlow to create deep learning models for finance - Build their own deep learning stock price prediction model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| t2t | T2T: Creating Sequence to Sequence Models for Generalized Learning | 7 hours | Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team. In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks. By the end of this training, participants will be able to: - Install tensor2tensor, select a data set, and train and evaluate an AI model - Customize a development environment using the tools and components included in Tensor2Tensor - Create and use a single model to concurrently learn a number of tasks from multiple domains - Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited - Obtain satisfactory processing results using a single GPU Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| aicityplanning | Artificial Intelligence for City Planning | 14 hours | What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly? In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining. Audience - City planners - Architects - Developers - Transportation officials Format of the Course - Part lecture, part discussion, and a series of interactive exercises. Note - To request a customized training for this course, please contact us to arrange. |
| h2oautoML | H2O AutoML | 14 hours | H2O AutoML is an artificial intelligence platform that automates the process of building, selecting and optimizing large numbers of machine learning models. This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters. By the end of this training, participants will be able to: - Automate the machine learning workflow. - Automatically train and tune many machine learning models within a specified time range. - Train stacked ensembles to arrive at highly predictive ensemble models. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. |
| autosklearn | AutoML with Auto-sklearn | 14 hours | Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters. This instructor-led, live training (onsite or remote) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model. By the end of this training, participants will be able to: - Automate the process of training highly efficient machine learning models. - Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models. - Use the power of machine learning to solve real-world business problems. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. |
| autokeras | AutoML with Auto-Keras | 14 hours | Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML). This instructor-led, live training (onsite or remote) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model. By the end of this training, participants will be able to: - Automate the process of training highly efficient machine learning models. - Automatically search for the best parameters for deep learning models. - Build highly accurate machine learning models. - Use the power of machine learning to solve real-world business problems. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. - To learn more about Auto-Keras, please visit: https://autokeras.com/ |
| automl | AutoML | 14 hours | AutoML is user-friendly machine learning software that automates much of the work needed to select an ideal machine learning algorithm, its parameter settings, and pre-processing methods. This instructor-led, live training (onsite or remote) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data. By the end of this training, participants will be able to: - Install and evaluate various open source AutoML tools. - Train high quality machine learning models. - Efficiently solve different types of supervised machine learning problems. - Write just the necessary code to initiate the automated machine learning process. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. - To learn more about AutoML, please visit: https://www.automl.org/ |
| appaipy | Applied AI from Scratch in Python | 28 hours | This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. |
| appai | Applied AI from Scratch | 28 hours | This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. |
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