Online or onsite, 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 "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 Machine Learning training can be carried out locally on customer premises in Denver or in NobleProg corporate training centers in Denver.
NobleProg -- Your Local Training Provider
CO, Denver - DTC Corporate Center III
7900 East Union Avenue Suite 1100, Denver, united states, 80237
The office in Denver Corporate is located at the heart of the Denver Tech Center - the district that is home to many of th...
The office in Denver Corporate is located at the heart of the Denver Tech Center - the district that is home to many of the city's major businesses and corporations. The Rocky Mountains can be seen from these 11th-floor offices, as well as the downtown skyline. The Denver Tech Center itself is strategically located at the junction between Interstates 225 and 25. It's minutes away from Belleview Light Rail station, with hotels and restaurants surrounding it. Its state-of-the-art office facilities come with beautiful mountain views.
This instructor-led, live training in Denver (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.By the end of this training, participants will be able to:
Understand the fundamentals of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimize chatbot performance over time.
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level developers, data scientists, and tech professionals who wish to leverage Google Vertex AI for creating and deploying custom AI models.By the end of this training, participants will be able to:
Understand the capabilities and features of Google Vertex AI.
Set up and configure the Google Vertex AI environment.
Develop and train custom AI models using Vertex AI.
Deploy and manage AI models on Google Cloud Platform.
Utilize Vertex AI's tools for monitoring and optimizing model performance.
Implement best practices for AI model development and deployment.
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.By the end of this training, participants will be able to:
Understand the role of Edge AI in financial services.
Implement fraud detection systems using Edge AI.
Enhance customer service through AI-driven solutions.
Apply Edge AI for risk management and decision-making.
Deploy and manage Edge AI solutions in financial environments.
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.By the end of this training, participants will be able to:
Explain what generative AI is and how it works.
Describe the transformer architecture that powers LLMs.
Use empirical scaling laws to optimize LLMs for different tasks and constraints.
Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
Discuss the opportunities and risks of generative AI for society and business.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner-level to intermediate-level data scientists and software developers who wish to learn about Milvus and its practical applications in various AI scenarios.By the end of this training, participants will be able to:
Understand the architecture and features of Milvus.
Implement vector databases in different AI applications.
Perform similarity searches with high accuracy and speed.
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.By the end of this training, participants will be able to:
Understand the role of Edge AI in industrial automation.
Implement predictive maintenance solutions using Edge AI.
Apply AI techniques for quality control in manufacturing processes.
Optimize industrial processes using Edge AI.
Deploy and manage Edge AI solutions in industrial environments.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner-level to intermediate-level data scientists and software developers who wish to learn how to apply Pinecone to various AI scenarios, from semantic search to personalization.By the end of this training, participants will be able to:
Understand the architecture and features of Pinecone.
Implement vector databases in different AI applications.
Perform similarity searches with high accuracy and speed.
This instructor-led, live training in Denver (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
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level to advanced-level data scientists and machine learning engineers who wish to master the use of vector databases for scalable, high-performance AI applications.By the end of this training, participants will be able to:
Understand the principles of vector databases.
Learn techniques for creating and managing vector embeddings.
Explore indexing strategies for high-dimensional data.
Develop skills for performing efficient similarity searches.
Apply vector database knowledge to machine learning projects.
This instructor-led, live training in (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.
By the end of this training, participants will be able to:
This instructor-led, live training in Denver (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.
By the end of this training, participants will be able to:
Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.
By the end of this training, participants will be able to:
Understand the principles of distributed deep learning.
Install and configure DeepSpeed.
Scale deep learning models on distributed hardware using DeepSpeed.
Implement and experiment with DeepSpeed features for optimization and memory efficiency.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.
By the end of this training, participants will be able to:
Set up a development environment that includes a popular LLM.
Create a basic LLM and fine-tune it on a custom dataset.
Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who wish to understand the inner workings of GPT models, explore the capabilities of GPT-3 and GPT-4, and learn how to leverage these models for their NLP tasks.
By the end of this training, participants will be able to:
Understand the key concepts and principles behind Generative Pre-trained Transformers.
Comprehend the architecture and training process of GPT models.
Utilize GPT-3 for tasks such as text generation, completion, and translation.
Explore the latest advancements in GPT-4 and its potential applications.
Apply GPT models to their own NLP projects and tasks.
This instructor-led, live training in Denver (online or onsite) is aimed at beginner to intermediate-level software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities.
By the end of this training, participants will be able to:
Understand how Vertex AI works and use it as a machine learning platform.
Learn about machine learning and NLP concepts.
Know how to train and deploy machine learning models using Vertex AI.
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.
In this instructor-led, live training in Denver, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
Implement machine learning algorithms and techniques for solving complex problems.
Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
Push Python algorithms to their maximum potential.
Use libraries and packages such as NumPy and Theano.
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.
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 instructor-led, live training in Denver (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
Apply advanced Reinforcement Learning algorithms to solve real-world problems.
In this instructor-led, live training in Denver, 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.
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
Explore how data is being interpreted by machine learning models
Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
Explore the properties of a specific embedding to understand the behavior of a model
Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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 instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
By the end of this training, participants will be able to:
Automate the machine learning workflow.
Automatically train and tune many machine learning models within a specified time range.
Train stacked ensembles to arrive at highly predictive ensemble models.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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 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 the scope of operations that can be run.
Deploy a deep learning model on an embedded device running Linux.
This instructor-led, live training in Denver (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.
This instructor-led, live training in (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's 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.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python.
By the end of this training, participants will be able to:
Apply deep learning with supervised or unsupervised learning methods.
Develop, train, and implement concurrent neural networks and recurrent neural networks.
Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.
This instructor-led, live training in Denver (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques.
By the end of this training, participants will be able to:
Use Keras to build and train a convolutional neural network.
Use computer vision techniques to identify lanes in an autonomos driving project.
Train a deep learning model to differentiate traffic signs.
Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.
In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.
By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.
Format of the Course
Overview of Machine and Deep Learning
In-class coding and integration exercises
Test questions sprinkled along the way to check understanding
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
understand Caffe’s structure and deployment mechanisms
carry out installation / production environment / architecture tasks and configuration
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.
After completing this course, delegates will:
understand SINGA’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
Deeplearning4j is the first commercial-grade, 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.
Audience
This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.
After this course delegates will be able to:
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.
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.
In this instructor-led, live training in Denver, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
Design and code DL for NLP using Python libraries.
Create Python code that reads a substantially huge collection of pictures and generates keywords.
Create Python Code that generates captions from the detected keywords.
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
Create a fraud detection model in Python and TensorFlow.
Build linear regressions and linear regression models to predict fraud.
Develop an end-to-end AI application for analyzing fraud data.
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
Use OpenShift to simplify the work of initializing a Kubernetes cluster.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This instructor-led, live training in Denver (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
Install and configure TensorFlow 2.x.
Understand the benefits of TensorFlow 2.x over previous versions.
Build deep learning models.
Implement an advanced image classifier.
Deploy a deep learning model to the cloud, mobile and IoT devices.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
Build and train machine learning models with TensorFlow.js.
Run existing machine learning models in the browser or under Node.js.
Retrain pre-existing machine learning using custom data.
In this instructor-led, live training in Denver (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
Train, export and serve various TensorFlow models.
Test and deploy algorithms using a single architecture and set of APIs.
Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.
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
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
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
Install and configure TFX and supporting third-party tools.
Use TFX to create and manage a complete ML production pipeline.
Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
Deploy machine learning features to web applications, mobile applications, IoT devices and more.
In this instructor-led, live training in Denver, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
Train various types of neural networks on large amounts of data.
Use TPUs to speed up the inference process by up to two orders of magnitude.
Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
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
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
have a good understanding on deep neural networks(DNN), CNN and RNN
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, building graphs and logging
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building machine learning models with AdaBoost.
Understand the ensemble learning approach and how to implement adaptive boosting.
Learn how to build AdaBoost models to boost machine learning algorithms in Python.
Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building machine learning models with Random forest.
Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
Evaluate and optimize machine learning model performance by tuning the hyperparameters.
This instructor-led, live training in (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing machine learning features for mobile apps.
Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at technical persons who wish to build machine learning models using algorithms such as GLM, Deep Learning and Random Forests.
By the end of this training, participants will be able to:
Install and configure H2O.
Create machine learning models using different popular algorithms.
Evaluate models based on the type of data and business requirements.
This instructor-led, live training in Denver (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.
By the end of this training, participants will be able to:
Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
Train high quality machine learning models.
Efficiently solve different types of supervised machine learning problems.
Write just the necessary code to initiate the automated machine learning process.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
By the end of this training, participants will be able to:
Explore the AutoML product line to implement different services for various data types.
Prepare and label datasets to create custom ML models.
Train and manage models to produce accurate and fair machine learning models.
Make predictions using trained models to meet business objectives and needs.
RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.
By the end of this training, participants will be able to:
Install and configure RapidMiner
Prepare and visualize data with RapidMiner
Validate machine learning models
Mashup data and create predictive models
Operationalize predictive analytics within a business process
Troubleshoot and optimize RapidMiner
Audience
Data scientists
Engineers
Developers
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 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.
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course
This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
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.
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.
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 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.
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.
Introduction:
Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.
This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.
Target Audience:
( Depending on target audience, course materials will be customized)
Executives
A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.
Project Managers
How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.
Developers
In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.
Salespersons
A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
This instructor-led, live training in Denver (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.
By the end of this training, participants will be able to:
Understand the core concepts of machine learning and deep learning.
Get insights on the future of business and industry with ML and DL.
Define business strategies and solutions with deep learning.
Learn how to apply data science and deep learning in solving business problems.
Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in finance
Use R to create deep learning models for finance
Build their own deep learning stock price prediction model using R
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in banking
Use Python, Keras, and TensorFlow to create deep learning models for banking
Build their own deep learning credit risk model using Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in banking
Use R to create deep learning models for banking
Build their own deep learning credit risk model using R
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in finance
Use Python, Keras, and TensorFlow to create deep learning models for finance
Build their own deep learning stock price prediction model using Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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 attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.
In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.
By the end of this training, participants will be able to:
Understand the fundamentals of Deep Learning
Learn Deep Learning techniques and their applications in the industry
Examine issues in medicine which can be solved by Deep Learning technologies
Explore Deep Learning case studies in medicine
Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine
Audience
Managers
Medical professionals in leadership roles
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 Denver (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.
By the end of this training, participants will be able to:
Install and configure the necessary development environment, software and libraries to begin developing.
Analyze MRI images using deep learning techniques such as CNNs.
Detect potential health conditions such as heart disease through MRI scan analysis.
Apply techniques such as image segmentation and CNN training to identify potential disease.
Identify the genomics of a disease using radiomics.
Build and deploy a deep learning application aimed at healthcare image analysis.
Type: Theoretical training with applications decided in advance with the students on Lasagne or Keras according to the educational group Teaching method: presentation, discussions and case studies Artificial intelligence, after having disrupted many scientific fields, has begun to revolutionize a large number of economic sectors (industry, medicine, communication, etc.). However, its presentation in the mainstream media is often a fantasy, very far from what the domains of Machine Learning or Deep Learning really are. The purpose of this training is to provide engineers who already have mastery of IT tools (including a basic software programming basis) with an introduction to Deep Learning as well as to its different areas of specialization and therefore to the main network architectures. existing today. If the mathematical basics are covered during the course, a BAC+2 level of mathematics is recommended for greater comfort. It is absolutely possible to ignore the mathematical axis and retain only a “system” vision, but this approach will enormously limit your understanding of the subject.
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.
By the end of this training, participants will be able to:
Build a deep learning model
Automate data labeling
Work with models from Caffe and TensorFlow-Keras
Train data using multiple GPUs, the cloud, or clusters
Audience
Developers
Engineers
Domain experts
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
This instructor-led, live training in Denver (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
View, load, and classify images and videos using OpenCV 4.
Implement deep learning in OpenCV 4 with TensorFlow and Keras.
Run deep learning models and generate impactful reports from images and videos.
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
Part lecture, part discussion, heavy hands-on practice
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
Set up and configure PaddlePaddle
Set up a Convolutional Neural Network (CNN) for image recognition and object detection
Set up a Recurrent Neural Network (RNN) for sentiment analysis
Set up deep learning on recommendation systems to help users find answers
Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.
By the end of this training, participants will be able to:
Understand and implement unsupervised learning techniques
Apply clustering and classification to make predictions based on real world data.
Visualize data to quicly gain insights, make decisions and further refine analysis.
Improve the performance of a machine learning model using hyper-parameter tuning.
Put a model into production for use in a larger application.
Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
This instructor-led, live training in Denver (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
Install and configure the necessary development environment, software and libraries to begin developing.
Build, train, and deploy deep learning models to analyze live video feeds.
Identify, track, segment and predict different objects within video frames.
Optimize object detection and tracking models.
Deploy an intelligent video analytics (IVA) application.
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
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 Denver (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.
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 Denver (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 Denver (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 instructor-led, live training in Denver (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 Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.
By the end of this training, participants will be able to:
Customize DeepMind Lab to build and run an environment that suits learning and training needs.
Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
Facilitate agent evaluation to develop intelligence in a 3D game-like world.
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.
This instructor-led, live training in Denver (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve 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.
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.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio.
By the end of this training, participants will be able to:
Build a GAN using machine learning libraries in Python.
This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.
This instructor-led, live training in Denver (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Denver (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.
By the end of this training, participants will be able to:
Gain an overview of artificial intelligence, machine learning, and computational intelligence.
Understand the concepts of neural networks and different learning methods.
Choose artificial intelligence approaches effectively for real-life problems.
Implement AI applications in mechatronic engineering.
This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.
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
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
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.
By the end of this training, participants will be able to:
Install ML.NET and integrate it into the application development environment.
Understand the machine learning principles behind ML.NET tools and algorithms.
Build and train machine learning models to perform predictions with the provided data smartly.
Evaluate the performance of a machine learning model using the ML.NET metrics.
Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
Apply the machine learning concepts of ML.NET to other data science applications.
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
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
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
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
Install and configure MLflow and related ML libraries and frameworks.
Appreciate the importance of trackability, reproducability and deployability of an ML model
Deploy ML models to different public clouds, platforms, or on-premise servers.
Scale the ML deployment process to accommodate multiple users collaborating on a project.
Set up a central registry to experiment with, reproduce, and deploy ML models.
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.
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.
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
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
This instructor-led, live training in Denver (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
Prepare, validate and version data for use by ML models.
Understand the components of an ML Pipeline and the tools needed to build one.
Experiment with different machine learning frameworks and servers for deploying to production.
Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
This instructor-led, live training in Denver (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.
By the end of this training, participants will be able to:
Build reproducible workflows and machine learning models.
Manage the machine learning lifecycle.
Track and report model version history, assets, and more.
Deploy production ready machine learning models anywhere.
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.
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.
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
Practical Quantum Computing: Live Online
Launch your high-tech career
This is a 10 hour instructor-led, live online training course. After your immersive training, you will be ready to start work as an entry level quantum computing developer.
By the end of this training, participants will be able to:
Run and test your quantum programs with the integrated IBM Q
Use Qiskit to create, compile, and execute quantum computing programs
Working with practical and advanced quantum algorithms such as QAOA
Recast real-world problems into an appropriate quantum computing language
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.
This instructor-led, live training in Denver (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.
By the end of this training, participants will be able to:
Create recommender systems at scale.
Apply collaborative filtering to build recommender systems.
Use Apache Spark to compute recommender systems on clusters.
Build a framework to test recommendation algorithms with Python.
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
Use a command-line tool that summarizes text.
Design and create Text Summarization code using Python libraries.
This instructor-led, live training in Denver (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.
This instructor-led, live training in Denver (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
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Testimonials (23)
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
Keeping it short and simple.
Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
Fantastic training, one of the best I have ever attended! The moderator, Rafal, provided excellent answers to the issues raised and explained all the methods very thoroughly. JestI am very satisfied and will gladly take advantage of the training conducted by this trainer again.
Darek Paszkowski - Orange Szkolenia Sp. z o.o.
Machine Translated
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
Los ejemplos y la paciencia del instructor
José Emilio Sánchez García - Universidad Autónoma Indígena de México
Course - Natural Language Processing with TensorFlow
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Bitcomp Sp. z o.o.
Course - Machine Learning on iOS
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Course - Kubeflow
That it was applying real company data.
Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
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
Course - Machine Learning Concepts for Entrepreneurs and Managers
Convolution filter
Francesco Ferrara
Course - Introduction to Machine Learning
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course - Machine Learning – Data science
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course - Python for Advanced Machine Learning
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course - Neural Networks Fundamentals using TensorFlow as Example
The trainer was so knowledgeable and included areas I was interested in.
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