Artificial Intelligence (AI) Training Courses in Malta

Artificial Intelligence (AI) Training Courses

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. Onsite live Artificial Intelligence (AI) trainings in Malta can be carried out locally on customer premises or in NobleProg corporate training centers.

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AI Course Outlines in Malta

Course Name
Duration
Overview
Course Name
Duration
Overview
7 hours
Overview
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.
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.
7 hours
Overview
The training is aimed at people who want to learn the basics of neural networks and their applications.
14 hours
Overview
This course is an introduction to applying neural networks in real world problems using R-project software.
21 hours
Overview
Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
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.
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.
14 hours
Overview
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.
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.
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.
21 hours
Overview
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.
21 hours
Overview
This course will be a combination of theory and practical work with specific examples used throughout the event.
21 hours
Overview
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.
21 hours
Overview
This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
21 hours
Overview
MATLAB is a numerical computing environment and programming language developed by MathWorks.
21 hours
Overview
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.
28 hours
Overview
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.
28 hours
Overview
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
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.
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
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.
21 hours
Overview
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- 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
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.
21 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.
28 hours
Overview
Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images.

Audience

This course is directed at engineers and architects aiming to utilize CNTK in their projects.
14 hours
Overview
Apache SystemML is a distributed and declarative machine learning platform.

SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.

Audience

This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
28 hours
Overview
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
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
35 hours
Overview
TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- 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, embedding terms, building graphs and logging
14 hours
Overview
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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