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 training can be carried out locally on customer premises in the Philippines or in NobleProg corporate training centers in the Philippines. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
Testimonials
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
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
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
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
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 trainer very easily explained difficult and advanced topics.
Leszek K
Course:Artificial Intelligence Overview
Translated by
All like it
蒙 李
Course:Machine Learning Fundamentals with Python
Translated by
way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Course:Machine Learning and Deep Learning
Translated by
Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Course:Machine Learning and Deep Learning
Translated by
Communication with lecturers
文欣 张
Course:Artificial Neural Networks, Machine Learning, Deep Thinking
Translated by
like it all
lisa xie
Course:Artificial Neural Networks, Machine Learning, Deep Thinking
Translated by
Machine Learning Subcategories
ML (Machine Learning) Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| aiauto | Artificial Intelligence in Automotive | 14 hours | 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. |
| 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 |
| octnp | Octave not only for programmers | 21 hours | 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. |
| mlrobot1 | Machine Learning for Robotics | 21 hours | This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software. |
| 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 |
| mlintro | Introduction to Machine Learning | 7 hours | 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. |
| MLFWR1 | Machine Learning Fundamentals with R | 14 hours | 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. |
| mlfunpython | Machine Learning Fundamentals with Python | 14 hours | 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. |
| mlfsas | Machine Learning Fundamentals with Scala and Apache Spark | 14 hours | 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. |
| 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 |
| mlentre | Machine Learning Concepts for Entrepreneurs and Managers | 21 hours | This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience - Investors and AI entrepreneurs - Managers and Engineers whose company is venturing into AI space - Business Analysts & Investors |
| mldt | Machine Learning and Deep Learning | 21 hours | This course covers AI (emphasizing Machine Learning and Deep Learning) |
| 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 |
| matlabml1 | Introduction to Machine Learning with MATLAB | 21 hours | MATLAB is a numerical computing environment and programming language developed by MathWorks. |
| 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. |
| intrdplrngrsneuing | Introduction Deep Learning & Neural Networks for Engineers | 21 hours | Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject. |
| facebooknmt | Facebook NMT: Setting up a Neural Machine Translation System | 7 hours | Facebook NMT (Fairseq) is an open-source sequence-to-sequence learning toolkit created by Facebook for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience - Localization specialists with a technical background - Global content managers - Localization engineers - Software developers in charge of implementing global content solutions Format of the course - Part lecture, part discussion, heavy hands-on practice Note - If you wish to use specific source and target language content, please contact us to arrange. |
| 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 |
| dsstne | Amazon DSSTNE: Build a Recommendation System | 7 hours | Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host. In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application. By the end of this training, participants will be able to: - Train a recommendation model with sparse datasets as input - Scale training and prediction models over multiple GPUs - Spread out computation and storage in a model-parallel fashion - Generate Amazon-like personalized product recommendations - Deploy a production-ready application that can scale at heavy workloads Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dmmlr | Data Mining & Machine Learning with R | 14 hours | 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. |
| datamodeling | Pattern Recognition | 35 hours | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| cortana | Turning Data into Intelligent Action with Cortana Intelligence | 28 hours | Cortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions. In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications. By the end of this training, participants will be able to: - Learn how to use Cortana Intelligence Suite tools - Acquire the latest knowledge of data management and analytics - Use Cortana components to turn data into intelligent action - Use Cortana to build applications from scratch and launch it on the cloud Audience - Data scientists - Programmers - Developers - Managers - Architects Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| bspkannmldt | Artificial Neural Networks, Machine Learning and Deep Thinking | 21 hours | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| bspkaml | Machine Learning | 21 hours | This course will be a combination of theory and practical work with specific examples used throughout the event. |
| appliedml | Applied Machine Learning | 14 hours | This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). 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 practical applications 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. |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 hours | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| aiintrozero | From Zero to AI | 35 hours | This course is created for people who have no previous experience in probability and statistics. |
| 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 |
Upcoming Machine Learning Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Artificial Intelligence in Automotive - Davao, Topaz Tower Centre | Thu, 2018-11-29 09:30 | 535012PHP / 544812PHP |
| Artificial Intelligence in Automotive - Manila, Cyber One Eastwood | Tue, 2019-01-08 09:30 | 535012PHP / 542812PHP |
| Artificial Intelligence in Automotive - Cebu, Apple One Equicom Tower | Tue, 2019-01-08 09:30 | 535012PHP / 540812PHP |
| Artificial Intelligence in Automotive - Davao, Topaz Tower Centre | Tue, 2019-01-29 09:30 | 535012PHP / 544812PHP |
| Artificial Intelligence in Automotive - Manila, Gateway Tower - Quezon City | Thu, 2019-02-14 09:30 | 535012PHP / 544812PHP |
Other regions
Other countries
Consulting
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Course Discounts
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Docker for Developers and System Administrators | Manila, Gateway Tower - Quezon City | Tue, 2018-12-11 09:30 | 94820PHP / 104620PHP |
| Cloud Computing Overview | Manila, Gateway Tower - Quezon City | Fri, 2018-12-28 09:30 | 47410PHP / 53060PHP |
| Ubuntu Server Overview | Manila, Gateway Tower - Quezon City | Wed, 2019-01-09 09:30 | 47410PHP / 53060PHP |
| Lua Embedding | Manila, Gateway Tower - Quezon City | Mon, 2019-02-04 09:30 | 94820PHP / 104620PHP |
Some of our clients
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is growing fast!
We are looking to expand our presence in Thailand!
As a Business Development Manager you will:
- expand business in Thailand
- recruit local talent (sales, agents, trainers, consultants)
- recruit local trainers and consultants
We offer:
- Artificial Intelligence and Big Data systems to support your local operation
- high-tech automation
- continuously upgraded course catalogue and content
- good fun in international team
If you are interested in running a high-tech, high-quality training and consulting business.
contact us right away!