Explore Our Courses

Advanced Machine Learning Models with Google Colab

 21 時間
Advanced machine learning models play a critical role in pushing the boundaries of AI and data science. This course delves into sophisticated techniques for building, optimizing, and deploying machine learning models using Google Colab, allowing participants to leverage powerful cloud-based tools for their projects.This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab.By the end of this training, participants will be able to:
  • Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
  • Optimize model performance through hyperparameter tuning.
  • Deploy machine learning models in real-world applications using Google Colab.
  • Collaborate and manage large-scale machine learning projects in Google Colab.
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.
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Optimizing AI Models for Edge Devices

 14 時間
Optimizing AI Models for Edge Devices focuses on techniques for optimizing AI models to run efficiently on edge hardware. This course covers model compression, quantization, and other optimization techniques, providing practical knowledge for building performant AI models for edge devices.This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.By the end of this training, participants will be able to:
  • Understand the challenges and requirements of deploying AI models on edge devices.
  • Apply model compression techniques to reduce the size and complexity of AI models.
  • Utilize quantization methods to enhance model efficiency on edge hardware.
  • Implement pruning and other optimization techniques to improve model performance.
  • Deploy optimized AI models on various edge devices.
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.
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Building AI Solutions on the Edge

 14 時間
Building AI Solutions on the Edge focuses on the step-by-step creation and deployment of AI models on edge devices. This course includes practical projects and real-world applications, providing participants with hands-on experience in developing and implementing AI solutions directly on edge hardware.This instructor-led, live training (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.By the end of this training, participants will be able to:
  • Understand the principles of Edge AI and its benefits.
  • Set up and configure the edge computing environment.
  • Develop, train, and optimize AI models for edge deployment.
  • Implement practical AI solutions on edge devices.
  • Evaluate and improve the performance of edge-deployed models.
  • Address ethical and security considerations in Edge AI applications.
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.
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Edge AI with TensorFlow Lite

 14 時間
TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. Edge AI with TensorFlow Lite focuses on utilizing TensorFlow Lite for developing and deploying Edge AI models. This course covers the tools and techniques specific to TensorFlow Lite, providing practical knowledge for building efficient AI models for edge devices.This instructor-led, live training (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.By the end of this training, participants will be able to:
  • Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
  • Develop and optimize AI models using TensorFlow Lite.
  • Deploy TensorFlow Lite models on various edge devices.
  • Utilize tools and techniques for model conversion and optimization.
  • Implement practical Edge AI applications using TensorFlow Lite.
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.
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AI-Powered Autonomous Systems

 21 時間
AI-powered autonomous systems are revolutionizing industries by enabling machines to operate independently and make real-time decisions. This course focuses on the design and implementation of these systems, covering self-driving cars, drones, and industrial robots, with an emphasis on safety and reliability.This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.By the end of this training, participants will be able to:
  • Design and implement AI models for autonomous decision-making.
  • Develop control algorithms for autonomous navigation and obstacle avoidance.
  • Ensure safety and reliability in AI-powered autonomous systems.
  • Integrate autonomous systems with existing robotics and AI frameworks.
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.
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AI-Enhanced Yield Management in Semiconductor Production

 14 時間
AI is transforming yield management in semiconductor production, enabling real-time data analysis and the use of machine learning algorithms to enhance yield rates and minimize production costs.This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.By the end of this training, participants will be able to:
  • Analyze production data to identify factors affecting yield rates.
  • Implement AI algorithms to enhance yield management processes.
  • Optimize production parameters to reduce defects and improve yields.
  • Integrate AI-driven yield management into existing production workflows.
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.
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Predictive Maintenance with AI in Semiconductor Manufacturing

 14 時間
AI is revolutionizing predictive maintenance in semiconductor manufacturing, allowing for the anticipation of equipment failures and the minimization of downtime through the use of predictive models.This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.By the end of this training, participants will be able to:
  • Implement AI models for predicting equipment failures in semiconductor manufacturing.
  • Analyze maintenance data to identify patterns and trends indicative of potential issues.
  • Integrate AI-driven predictive maintenance into existing manufacturing workflows.
  • Reduce downtime and maintenance costs through proactive equipment management.
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.
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Advanced AI Techniques for Semiconductor Design Automation

 21 時間
AI is increasingly integral to the design and verification of semiconductor chips, with advanced techniques such as machine learning and neural networks enhancing the capabilities of Electronic Design Automation (EDA) tools.This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.By the end of this training, participants will be able to:
  • Apply advanced AI techniques to optimize semiconductor design processes.
  • Integrate machine learning models into EDA tools for enhanced design verification.
  • Develop AI-driven solutions for complex design challenges in chip fabrication.
  • Leverage neural networks for improving the accuracy and speed of design automation.
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.
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AI-Driven Process Optimization in Chip Fabrication

 14 時間
AI is revolutionizing semiconductor fabrication by optimizing processes, enhancing yield, detecting defects, and fine-tuning process parameters for better outcomes.This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.By the end of this training, participants will be able to:
  • Understand AI methodologies for process optimization in chip fabrication.
  • Implement AI models to enhance yield and reduce defects.
  • Analyze process data to identify key parameters for optimization.
  • Apply machine learning techniques to fine-tune semiconductor manufacturing processes.
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.
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Introduction to AI in Semiconductor Manufacturing

 14 時間
AI is revolutionizing semiconductor manufacturing by improving production efficiency, enhancing quality control, and optimizing various processes.This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.By the end of this training, participants will be able to:
  • Understand the basic principles of AI and how they apply to semiconductor manufacturing.
  • Identify areas within semiconductor manufacturing where AI can be effectively implemented.
  • Utilize AI tools and techniques to enhance production efficiency and quality control.
  • Implement basic AI models to optimize manufacturing processes.
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.
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AI-Powered Cybersecurity: Advanced Threat Detection & Response

 28 時間
AI-Powered Cybersecurity at an advanced level focuses on the use of sophisticated AI techniques to detect, analyze, and respond to complex cyber threats. This course delves into advanced algorithms, real-time threat detection, and the customization of AI models for enhanced security operations.This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate their skills in AI-driven threat detection and incident response.By the end of this training, participants will be able to:
  • Implement advanced AI algorithms for real-time threat detection.
  • Customize AI models for specific cybersecurity challenges.
  • Develop automation workflows for threat response.
  • Secure AI-driven security tools against adversarial attacks.
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.
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Machine Learning with Google Colab

 14 時間
Google Colab is a cloud-based platform that provides a collaborative environment for machine learning development, offering free access to computing resources and an easy-to-use interface.This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.By the end of this training, participants will be able to:
  • Set up and navigate Google Colab for machine learning projects.
  • Understand and apply various machine learning algorithms.
  • Use libraries like Scikit-learn to analyze and predict data.
  • Implement supervised and unsupervised learning models.
  • Optimize and evaluate machine learning models effectively.
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.
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Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

 21 時間
Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.By the end of this training, participants will be able to:
  • Understand advanced deep learning architectures and techniques for text-to-image generation.
  • Implement complex models and optimizations for high-quality image synthesis.
  • Optimize performance and scalability for large datasets and complex models.
  • Tune hyperparameters for better model performance and generalization.
  • Integrate Stable Diffusion with other deep learning frameworks and tools.
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
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AI-Powered Cybersecurity: Threat Detection & Response

 21 時間
AI-Powered Cybersecurity combines artificial intelligence with cybersecurity to enhance threat detection, response, and prevention. This course explores AI techniques in identifying and mitigating cyber threats, equipping participants with hands-on skills in implementing AI-driven security measures.This instructor-led, live training (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.By the end of this training, participants will be able to:
  • Understand AI applications in cybersecurity.
  • Implement AI algorithms for threat detection.
  • Automate incident response with AI tools.
  • Integrate AI into existing cybersecurity infrastructure.
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.
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LightGBM for Machine Learning

 21 時間
LightGBM is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification, and other machine learning tasks. This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques.By the end of this training, participants will be able to:
  • Install and configure LightGBM.
  • Understand the theory behind gradient boosting and decision tree algorithms
  • Use LightGBM for basic and advanced machine learning tasks.
  • Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
  • Integrate LightGBM with other machine learning frameworks.
  • Troubleshoot common issues in LightGBM.
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.
続きを読む...

Introduction to Stable Diffusion for Text-to-Image Generation

 21 時間
Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.By the end of this training, participants will be able to:
  • Understand the principles of Stable Diffusion and how it works for image generation.
  • Build and train Stable Diffusion models for image generation tasks.
  • Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
  • Optimize the performance and stability of Stable Diffusion 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
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AlphaFold

 7 時間
AlphaFold is an Artificial Intelligence (AI) system that performs the prediction of protein structures. It is developed by Alphabet’s/Google’s DeepMind as a deep learning system that can accurately predict 3D models of protein structures.This instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.By the end of this training, participants will be able to:
  • Understand the basic principles of AlphaFold.
  • Learn how AlphaFold works.
  • Learn how to interpret AlphaFold predictions and results.
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.
続きを読む...

Machine Learning with Python – 2 Days

 14 時間
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.
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Machine Learning with Python – 4 Days

 28 時間
The aim of this course is to provide general 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.
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Algebra for Machine Learning

 14 時間
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.
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Applied Machine Learning

 14 時間
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.
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Machine Learning

 21 時間
This course will be a combination of theory and practical work with specific examples used throughout the event.
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Dataiku for Enterprise AI and Machine Learning

 21 時間
Dataiku Data Science Studio (Dataiku DSS) is a centralized platform for operating and implementing machine learning models in enterprise applications. It also allows its users to collaborate and iterate on various AI and ML approaches using computation abstraction features. Dataiku DSS, along with its supported instances on different cloud services, provides a solution to big data management that is aligned with the goals of the business. This instructor-led, live training (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.
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.
続きを読む...

Feature Engineering for Machine Learning

 14 時間
Feature Engineering is the process of selecting and transforming data to improve the accuracy of machine learning algorithms. It requires deep familiarity with the data on the part of a subject matter expert. This instructor-led, live training (online or onsite) is aimed at persons who wish to apply feature engineering techniques to better process data and obtain better machine learning models. By the end of this training, participants will be able to:
  • Set up an optimal development environment, including all needed Python packages.
  • Obtain important insights by analyzing the features of a data set.
  • Optimize machine learning models through adaptation of the raw data itself.
  • Clean and transform data sets in preparation for machine learning.
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.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 時間
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 telecom 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 telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
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.
続きを読む...

Machine Learning Algorithms in Julia

 21 時間
This instructor-led, live training (online or onsite) is aimed at people that already have a background in data science and statistics.  Format of the Course
  • Interactive lecture and discussion.
  • Exercises and practice.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Mathematica for Machine Learning

 14 時間
Mathematica is a modern computing system for data analytics. Mathematica offers built in machine learning capabilities for data analysis. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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.
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Machine Learning for Banking (with Python)

 21 時間
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
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Machine Learning and Big Data

 7 時間
This instructor-led, live training (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data. By the end of this training, participants will:
  • Understand the evolution and trends for machine learning.
  • Know how machine learning is being used across different industries.
  • Become familiar with the tools, skills and services available to implement machine learning within an organization.
  • Understand how machine learning can be used to enhance data mining and analysis.
  • Learn what a data middle backend is, and how it is being used by businesses.
  • Understand the role that big data and intelligent applications are playing across industries.
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.
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Machine Learning Concepts for Entrepreneurs and Managers

 21 時間
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
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. Business Analysts & Investors
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Machine Learning for Finance (with Python)

 21 時間
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
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Machine Learning Fundamentals with Scala and Apache Spark

 14 時間
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.
続きを読む...

Introduction to Machine Learning

 7 時間
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.
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Machine Learning on iOS

 14 時間
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
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Machine Learning in business – AI/Robotics

 14 時間
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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Machine Learning for Robotics

 21 時間
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.
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XGBoost for Gradient Boosting

 14 時間
XGBoost is a decision-tree-based ensemble Machine Learning algorithm. It uses a gradient boosting framework for solving prediction problems involving unstructured data such as images and text. Gradient boosting is also a popular technique for efficient modeling of tabular datasets. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use XGBoost to build models that efficiently solve regression, classification, ranking, and prediction problems. By the end of this training, participants will be able to:
  • Install and configure XGBoost.
  • Understand the relationship between decision trees and other algorithms such as logistic regression and random forest.
  • Test different libraries to determine the best one for the job.
  • Choose the right configuration for an algorithm.
  • Tune the hyper-parameters of an algorithm for a given dataset.
  • Implement a machine learning solution that balances power with complexity, explainability, and ease of implementation.
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.
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DataRobot

 7 時間
DataRobot is a machine learning platform that streamlines the building and deployment of predictive models. DataRobot accelerates predictive analytics, helping businesses make smarter decisions.This instructor-led, live training (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.By the end of this training, participants will be able to:
  • Load datasets in DataRobot to analyze, assess, and quality check data.
  • Build and train models to identify important variables and meet prediction targets.
  • Interpret models to create valuable insights that are useful in making business decisions.
  • Monitor and manage models to maintain an optimized prediction performance.
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.
続きを読む...

H2O AutoML

 14 時間
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 (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automate 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.
続きを読む...

AutoML with Auto-sklearn

 14 時間
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 (online or onsite) 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.
続きを読む...

AutoML with Auto-Keras

 14 時間
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 (online or onsite) 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/
続きを読む...

TensorFlow Lite for Embedded Linux

 21 時間
TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources. This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices. By the end of this training, participants will be able to:
  • Install and configure Tensorflow Lite on an embedded device.
  • Understand the concepts and components underlying TensorFlow Lite.
  • Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
  • Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
  • Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
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.
続きを読む...

TensorFlow Lite for Android

 21 時間
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems. This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow, machine learning and deep learning.
  • Load TensorFlow Models onto an Android device.
  • Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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 TensorFlow, please visit: https://www.tensorflow.org/lite/
続きを読む...

TensorFlow Lite for iOS

 21 時間
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems. This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow and machine learning on mobile devices.
  • Load TensorFlow Models onto an iOS device.
  • Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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.
続きを読む...

Tensorflow Lite for Microcontrollers

 21 時間
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.By the end of this training, participants will be able to:
  • Install TensorFlow Lite.
  • Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
  • Add AI to hardware devices without relying on network connectivity.
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.
続きを読む...

Deep Learning Neural Networks with Chainer

 14 時間
Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.By the end of this training, participants will be able to:
  • Set up the necessary development environment to start developing neural network models.
  • Define and implement neural network models using a comprehensible source code.
  • Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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.
続きを読む...

Distributed Deep Learning with Horovod

 7 時間
Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.By the end of this training, participants will be able to:
  • Set up the necessary development environment to start running deep learning trainings.
  • Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
  • Scale deep learning training with Horovod to run on multiple GPUs.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • This course is focused on Horovod, but other software tools and frameworks such as TensorFlow, Keras, PyTorch, and Apache MXNet may be required. Please let us know if you have specific requirements or preferences.
  • To request a customized training for this course, please contact us to arrange.
続きを読む...

Accelerating Deep Learning with FPGA and OpenVINO

 35 時間
An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.By the end of this training, participants will be able to:
  • Install the OpenVINO toolkit.
  • Accelerate a computer vision application using an FPGA.
  • Execute different CNN layers on the FPGA.
  • Scale the application across multiple nodes in a Kubernetes cluster.
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.
続きを読む...

Building Deep Learning Models with Apache MXNet

 21 時間
MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.By the end of this training, participants will be able to:
  • Install and configure Apache MXNet and its components.
  • Understand MXNet's architecture and data structures.
  • Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
  • Build a convolutional neural network for image classification.
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.
続きを読む...

Deep Learning with Keras

 21 時間
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.By the end of this training, participants will be able to:
  • Install and configure Keras.
  • Quickly prototype deep learning models.
  • Implement a convolutional network.
  • Implement a recurrent network.
  • Execute a deep learning model on both a CPU and GPU.
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 Keras, please visit: https://keras.io/
続きを読む...

Machine Learning with Python and Pandas

 14 時間
Pandas is a Python library for data manipulation and analysis. Using Pandas, users can perform predictive analysis through machine learning. This instructor-led, live training (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
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.
続きを読む...

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お客様の声(23)

Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.

Rick Johnson - Laramie County Community College

コース - Artificial Intelligence (AI) Overview

the VM is a nice idea

Vincent - REGNOLOGY ROMANIA S.R.L.

コース - Fundamentals of Artificial Intelligence (AI) and Machine Learning

I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.

Jenna - TCMT

コース - Machine Learning with Python – 2 Days

The clarity with which it was presented

John McLemore - Motorola Solutions

コース - Deep Learning for Telecom (with Python)

The way of transferring knowledge and the knowledge of the trainer.

Jakub Rękas - Bitcomp Sp. z o.o.

コース - Machine Learning on iOS

The explaination

Wei Yang Teo - Ministry of Defence, Singapore

コース - Machine Learning with Python – 4 Days

The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.

Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH

コース - Machine Learning Concepts for Entrepreneurs and Managers

The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.

Robert Baker

コース - Deep Learning with TensorFlow 2.0

Convolution filter

Francesco Ferrara

コース - Introduction to Machine Learning

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

コース - TensorFlow Extended (TFX)

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

コース - Natural Language Processing with TensorFlow

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

コース - Applied AI from Scratch in Python

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

コース - Machine Learning

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

コース - Python for Advanced Machine Learning

Very flexible.

Frank Ueltzhöffer

コース - Artificial Neural Networks, Machine Learning and Deep Thinking

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

コース - TensorFlow for Image Recognition

I liked the new insights in deep machine learning.

Josip Arneric

コース - Neural Network in 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

コース - Machine Learning and Deep Learning

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

コース - Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple

The global overview of deep learning.

Bruno Charbonnier

コース - Advanced Deep Learning

The topic is very interesting.

Wojciech Baranowski

コース - Introduction to Deep Learning

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

コース - Introduction to the use of neural networks

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

コース - Artificial Neural Networks, Machine Learning, Deep Thinking

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