Machine Learning with Random Forest Training Course

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.

AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.

In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.

Audience

• Network engineers
• Network operations personnel
• Telecom technical managers

Format of the course

• Part lecture, part discussion, hands-on exercises

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.

This course is created for people who have no previous experience in probability and statistics.

Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.

In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.

By the end of this training, participants will be able to:

• Understand fundamental linear algebra concepts
• Learn the linear algebra skills needed for machine learning
• Use linear algebra structures and concepts when working with data, images, algorithms, etc.
• Solve a machine learning problem using linear algebra

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Austria (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

By the end of this training, participants will be able to:

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

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.

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. 

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. 

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.

This instructor-led, live training in Austria (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.

By the end of this training, participants will be able to:

• Use notebook instances to prepare and upload data for training.
• Train machine learning models using training datasets.
• Deploy trained models to an endpoint to create predictions.

This instructor-led, live training in Austria (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.

By the end of this training, participants will be able to:

• Build machine learning models with zero programming experience.
• Create predictive algorithms with Azure Machine Learning.
• Deploy production ready machine learning algorithms.

This course will be a combination of theory and practical work with specific examples used throughout the event.

This instructor-led, live training in Austria (online or onsite) is aimed at web developers who wish to create machine learning models with Core ML for iOS 11 and macOS High Sierra development.

By the end of this training, participants will be able to:

• Build AI driven applications that use machine learning.
• Implement machine learning models that classify images.
• Use the Core ML API for supporting custom workflows and advanced use cases
• Analyze natural language text with machine learning models.

This instructor-led, live training in Austria (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives.

By the end of this training, participants will be able to:

• Install and configure Dataiku DSS upon their preferred operating system.
• Understand AI/ML concepts and principles that are fundamental to Dataiku features.
• Create and implement their own data visualization codes in Dataiku DSS projects.
• Deploy ML models and pipelines into production environments built around Dataiku DSS.
• Optimize the agility and flexibility of data analysis methods of their enterprise applications.
• Utilize Dataiku DSS to secure and manage how data flows through further business systems.