Introduction to Machine Learning Training Course

This instructor-led, live training (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: - Set up the required development environment to begin building machine learning models with AdaBoost. - Understand the ensemble learning methodology and how to implement adaptive boosting techniques. - Learn how to construct AdaBoost models to enhance machine learning algorithms in Python. - Utilize hyperparameter tuning to improve the accuracy and performance of AdaBoost models, ensuring they meet the standards required for government applications.

This instructor-led, live training in US (online or onsite) is aimed at data scientists and less technical individuals who wish to utilize Auto-Keras to automate the process of selecting and optimizing a machine learning model for government applications. 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. - Apply machine learning solutions to address real-world business challenges in a public sector context.

This instructor-led, live training (available online or on-site) is designed for technical professionals with a background in machine learning who seek to enhance their skills in optimizing models for detecting intricate patterns in large datasets using AutoML frameworks. The training aims to equip participants with the knowledge and practical tools necessary to improve model performance and efficiency, ensuring alignment with public sector workflows and governance standards for government applications.

This instructor-led, live training in [location] (online or onsite) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications, including those for government use. 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 tools. - 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 (online or onsite) provides an introduction to the field of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics for government use. By the end of this training, participants will be able to: - Apply core statistical methods to pattern recognition tasks. - Utilize key models such as neural networks and kernel methods for data analysis. - Implement advanced techniques to address complex problem-solving scenarios. - Enhance prediction accuracy by integrating different models.

This instructor-led, live training (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities for government applications. By the end of this training, participants will be able to: - Load datasets in DataRobin to analyze, assess, and quality check data. - Build and train models to identify key variables and meet prediction targets. - Interpret models to derive actionable insights that support informed decision-making. - Monitor and manage models to ensure ongoing optimization and performance.

This instructor-led, live training (online or onsite) is designed for government 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 develop custom ML models. - Train and manage models to ensure they produce accurate and fair results. - Utilize trained models to make predictions that align with business objectives and needs for government.

This instructor-led, live training in US (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 both 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 comprehensive machine learning pipelines across various architectures and cloud environments. - Utilize Kubeflow to spawn and manage Jupyter notebooks. - Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server for government. By the end of this training, participants will be able to: - Install and configure Kubernetes, Kubeflow, and other necessary software on AWS. - Utilize EKS (Elastic Kubernetes Service) to simplify the initialization of a Kubernetes cluster on AWS. - Develop and deploy a Kubernetes pipeline for automating and managing ML models in production environments. - Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel. - Leverage other AWS managed services to enhance an ML application.

This instructor-led, live training (available online or onsite) is designed for data scientists who aim to enhance their machine learning (ML) model creation, tracking, and deployment processes beyond the initial development phase. By the end of this training, participants will be able to: - Install and configure MLflow along with related ML libraries and frameworks. - Understand the significance of trackability, reproducibility, and deployability in the context of an ML model for government applications. - Deploy ML models to various public clouds, platforms, or on-premise servers. - Scale the ML deployment process to support multiple users collaborating on a project. - Set up a central registry to experiment with, reproduce, and deploy ML models efficiently.

This instructor-led, live training (available online or onsite) is designed for developers who wish to utilize Google’s ML Kit to build machine learning models optimized for mobile devices. By the end of this training, participants will be able to: - Set up the required development environment to begin developing machine learning features for mobile applications. - Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. - Enhance and optimize existing applications using the ML Kit SDK for on-device processing and deployment, ensuring alignment with public sector workflows and governance standards 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 (online or onsite) is designed for government data scientists and software engineers who wish to utilize Random Forest to develop machine learning algorithms for large datasets.

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

• Set up the required development environment to begin building machine learning models with Random Forest.
• Comprehend the benefits of Random Forest and how to apply it to address classification and regression challenges.
• Learn techniques for managing large datasets and interpreting multiple decision trees within Random Forest.
• Evaluate and enhance machine learning model performance through hyperparameter tuning.

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

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 - Combine data sources and create predictive models - Integrate 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 tailored to specific needs, please contact us to arrange.