Introduction to Machine Learning Training Course

This instructor-led, live training in US Empire (online or onsite) is designed for data scientists and software engineers who wish to utilize AdaBoost to develop boosting algorithms for machine learning with Python.

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

• Establish the required development environment to begin building machine learning models with AdaBoost.
• Gain a comprehensive understanding of the ensemble learning methodology and how to implement adaptive boosting techniques.
• Acquire the skills to construct AdaBoost models to enhance machine learning algorithms in Python.
• Apply hyperparameter tuning to optimize the accuracy and performance of AdaBoost models for government applications.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists and less technical personnel who wish to utilize Auto-Keras to automate the process of selecting and optimizing a machine learning model for government use.

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

• Automate the process of training highly efficient machine learning models for government applications.
• Conduct automatic searches for the best parameters for deep learning models to enhance governmental workflows.
• Develop highly accurate machine learning models that align with public sector needs.
• Leverage the power of machine learning to address real-world challenges in the public sector.

This instructor-led, live training (online or onsite) is designed for technical professionals with a background in machine learning. The course aims to enhance their skills in optimizing models for the detection of complex patterns in big data using AutoML frameworks, specifically tailored for government applications.

This instructor-led, live training in US Empire (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 government applications.

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

• Understand the fundamentals of chatbot development for government use.
• Navigate the Google Cloud Platform and access AutoML for government projects.
• Prepare data for training chatbot models in a public sector context.
• Train and evaluate custom chatbot models using AutoML, tailored to government needs.
• Deploy and integrate chatbots into various platforms and channels used by government agencies.
• Monitor and optimize chatbot performance over time to ensure alignment with public sector workflows and governance.

This instructor-led, live training in US Empire (online or onsite) provides an introduction to 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 for government.

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

• Apply core statistical methods to pattern recognition for government use.
• Use key models like neural networks and kernel methods for data analysis in public sector contexts.
• Implement advanced techniques for complex problem-solving within government workflows.
• Improve prediction accuracy by combining different models, enhancing governance and accountability.

This instructor-led, live training in US Empire (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 for government.

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

• Load datasets in DataRobot to analyze, assess, and ensure data quality.
• Build and train models to identify key variables and achieve prediction targets.
• Interpret model outputs to generate actionable insights that support informed decision-making.
• Monitor and manage models to maintain optimal predictive performance.

This instructor-led, live training in US Empire (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 machine learning models with minimal effort for government use.

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

• Evaluate the AutoML product line to implement various services tailored to different data types for government applications.
• Prepare and label datasets to develop custom machine learning models aligned with public sector workflows and governance.
• Train and manage models to ensure they produce accurate and fair results, adhering to accountability standards.
• Utilize trained models to make predictions that meet the specific objectives and needs of government agencies.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government.

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 for government.
• Run entire machine learning pipelines on diverse architectures and cloud environments for government.
• Use Kubeflow to spawn and manage Jupyter notebooks for government.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms for government.

This instructor-led, live training in US Empire (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server for government use.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS for government applications.
• Use EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS for government operations.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production environments for government.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel for government projects.
• Leverage other AWS managed services to enhance an ML application for government use cases.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go beyond building machine learning (ML) models and optimize the entire ML model creation, tracking, and deployment process for government use.

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

• Install and configure MLflow and related ML libraries and frameworks for government.
• Understand the importance of trackability, reproducibility, and deployability of an ML model in a public sector context.
• Deploy ML models to different public clouds, platforms, or on-premise servers suitable for government operations.
• Scale the ML deployment process to accommodate multiple users collaborating on a project within a government environment.
• Set up a central registry to experiment with, reproduce, and deploy ML models in alignment with government workflows and governance requirements.

This instructor-led, live training (online or onsite) is aimed at developers who wish to utilize Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Set up the required development environment to begin integrating machine learning features into mobile applications.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
• Enhance and optimize existing applications by leveraging the ML Kit SDK for on-device processing and deployment, ensuring alignment with public sector workflows and governance standards.

This training is tailored to meet the needs of developers working in environments that require robust, efficient, and secure mobile solutions for government.

Pattern Matching is a technique utilized to identify specified patterns within an image. It can be employed to determine the presence of specific characteristics within a captured image, such as the expected label on a defective product in a factory line or the precise dimensions of a component. This differs from "Pattern Recognition," which identifies general patterns based on larger collections of related samples, as Pattern Matching specifically defines what is being sought and confirms 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 for government applications.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to develop machine learning algorithms for large datasets.

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

• Configure the required development environment to initiate building machine learning models with Random Forest.
• Comprehend the benefits of Random Forest and its application in addressing classification and regression challenges.
• Manage large datasets and interpret multiple decision trees within the Random Forest framework.
• Assess and enhance machine learning model performance through hyperparameter tuning, ensuring optimal results for government applications.

This instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and leverage analytical tools for time series forecasting for government.

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

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Utilize RapidMiner to estimate and project values, and employ analytical tools for time series forecasting.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It offers 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 the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Integrate data sources and create predictive models
• Incorporate predictive analytics into business processes
• Troubleshoot and optimize RapidMiner for government applications

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

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

Note

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