Machine Learning Training in St. Louis

This instructor-led, live training (offered online or onsite) is designed for government professionals at the beginner level who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch. By the end of this training, participants will be able to: - Understand the concept and benefits of pre-trained models for government applications. - Explore various pre-trained model architectures and their use cases in public sector workflows. - Fine-tune a pre-trained model for specific tasks relevant to governmental operations. - Implement pre-trained models in simple machine learning projects to enhance efficiency and accuracy in government services.

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 in [location] (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 for government applications. 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 for government use.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who seek to acquire practical skills in deploying artificial intelligence (AI) models on edge devices for a variety of applications. By the end of this training, participants will be able to: - Understand the principles of Edge AI and its benefits. - Set up and configure the edge computing environment. - Develop, train, and optimize AI models for deployment on edge devices. - Implement practical AI solutions on edge devices. - Evaluate and enhance the performance of models deployed at the edge. - Address ethical and security considerations in Edge AI applications. This training is tailored to align with public sector workflows, governance, and accountability, ensuring that participants are well-equipped to apply these skills effectively for government projects.

This instructor-led, live training (offered online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience. The goal is to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices. By the end of this training, participants will be able to: - Optimize DeepSeek models for efficiency, accuracy, and scalability. - Implement best practices for MLOps and model versioning. - Deploy DeepSeek models on cloud and on-premise infrastructure. - Monitor, maintain, and scale AI solutions effectively, ensuring alignment with public sector workflows and governance for government.

MLOps on Kubernetes is a framework designed to automate the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows. This instructor-led, live training (available online or onsite) is targeted at intermediate-level practitioners who wish to develop automated, scalable MLOps pipelines on Kubernetes for government use. Upon completion of this training, participants will be equipped to: - Design end-to-end CI/CD pipelines for machine learning. - Implement GitOps workflows for model deployment and versioning. - Automate the training, testing, and packaging of ML models. - Integrate monitoring, alerting, and rollback strategies. **Format of the Course** - Instructor-guided presentations and technical deep dives. - Hands-on exercises that build real-world CI/CD workflows. - Live-lab practice deploying ML workloads to Kubernetes. **Course Customization Options** - Organizations may request tailored content aligned with their internal MLOps tools and infrastructure for government operations.

Kubeflow is an open-source platform designed to streamline the development, training, and deployment of machine learning workloads on Kubernetes. This instructor-led, live training (online or onsite) is aimed at professionals at the beginner to intermediate levels who wish to build reliable ML workflows using Kubeflow for government applications. Upon completion of this training, attendees will gain the skills to: - Navigate the Kubeflow ecosystem and its core components. - Build reproducible workflows with Kubeflow Pipelines. - Run scalable training jobs on Kubernetes. - Serve machine learning models efficiently using Kubeflow Serving. **Format of the Course** - Guided presentations and collaborative discussions. - Hands-on labs with real Kubeflow components. - Practical exercises to build end-to-end ML workflows. **Course Customization Options** - Customized versions of this training can be arranged to align with your team’s technology stack and project requirements for government use.

TinyML involves deploying optimized machine learning models on resource-constrained edge devices.

This instructor-led, live training (available online or onsite) is designed for advanced technical professionals who aim to design, optimize, and deploy complete TinyML pipelines for government applications.

By the conclusion of this training, participants will learn how to:

• Collect, prepare, and manage datasets suitable for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models to lightweight formats appropriate for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Format of the Course

• Instructor-guided lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Course Customization Options

• To tailor the training with specific toolchains, hardware boards, or internal workflows for government use, please contact us to arrange.

This instructor-led, live training in St. Louis (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications for government.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI for government.
• Develop and optimize AI models using TensorFlow Lite for deployment in government settings.
• Deploy TensorFlow Lite models on various edge devices used in public sector applications.
• Utilize tools and techniques for model conversion and optimization suitable for government workflows.
• Implement practical Edge AI applications using TensorFlow Lite to enhance public sector operations.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who wish to master the technologies behind autonomous systems for government applications. By the end of this training, participants will be able to: - Design and implement AI models for autonomous decision-making. - Develop control algorithms for autonomous navigation and obstacle avoidance. - Ensure safety and reliability in AI-powered autonomous systems. - Integrate autonomous systems with existing robotics and AI frameworks for government use.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who aim to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab. By the end of this training, participants will be able to: - Build and train convolutional neural networks (CNNs) using TensorFlow. - Leverage Google Colab for scalable and efficient cloud-based model development. - Implement image preprocessing techniques for computer vision tasks. - Deploy computer vision models for real-world applications. - Use transfer learning to enhance the performance of CNN models. - Visualize and interpret the results of image classification models. This training is tailored to ensure that professionals can apply these skills effectively in their roles, particularly in projects for government.

This instructor-led, live training (conducted online or onsite) is designed for advanced-level professionals who wish to deepen their understanding of machine learning models, refine their hyperparameter tuning skills, and learn effective model deployment using Google Colab. By the end of this training, participants will be able to: - Implement advanced machine learning models using popular frameworks such as Scikit-learn and TensorFlow. - Enhance model performance through hyperparameter tuning. - Deploy machine learning models in real-world applications using Google Colab. - Collaborate and manage large-scale machine learning projects in Google Colab, ensuring alignment with public sector workflows for government.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level professionals who aim to apply artificial intelligence (AI) techniques to optimize yield management in semiconductor manufacturing. By the end of this training, participants will be able to: - Analyze production data to identify factors impacting yield rates. - Implement AI algorithms to enhance yield management processes. - Optimize production parameters to minimize defects and improve yields. - Integrate AI-driven yield management into existing production workflows for government and industry applications.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools for government. By the end of this training, participants will be able to: - Understand how machine learning fits within AI and business strategy. - Apply supervised and unsupervised learning techniques to structured business problems. - Preprocess and transform data for modeling. - Use neural networks for classification and prediction tasks. - Perform sales forecasting using statistical and ML-based methods. - Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals in the semiconductor manufacturing sector who wish to apply AI-driven predictive maintenance techniques to enhance production efficiency and reduce unexpected equipment failures for government and private sector operations. By the end of this training, participants will be able to: - Implement AI models for predicting equipment failures in semiconductor manufacturing. - Analyze maintenance data to identify patterns and trends indicative of potential issues. - Integrate AI-driven predictive maintenance into existing manufacturing workflows for government and industry standards. - Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who aim to apply cutting-edge artificial intelligence techniques to semiconductor design automation. The goal is to enhance efficiency, accuracy, and innovation in chip design and verification processes. By the end of this training, participants will be able to: - Implement advanced AI methods to optimize semiconductor design workflows. - Integrate machine learning models into Electronic Design Automation (EDA) tools for improved design verification. - Create AI-driven solutions to address complex challenges in chip fabrication. - Utilize neural networks to enhance the precision and speed of design automation techniques, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data scientists and developers who aim to understand and apply deep learning techniques using the Google Colab environment. By the end of this training, participants will be able to: - Set up and navigate Google Colab for government deep learning projects. - Understand the fundamentals of neural networks. - Implement deep learning models using TensorFlow. - Train and evaluate deep learning models. - Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to understand and apply artificial intelligence (AI) techniques for optimizing semiconductor fabrication processes. By the end of this training, participants will be able to: - Understand AI methodologies for process optimization in chip fabrication. - Implement AI models to improve yield and minimize defects. - Analyze process data to identify critical parameters for optimization. - Apply machine learning techniques to refine semiconductor manufacturing processes, ensuring alignment with best practices for government and industry standards.

This instructor-led, live training (available online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow. By the end of this training, participants will be able to: - Set up Apache Airflow for government use in orchestrating machine learning workflows. - Automate data preprocessing, model training, and validation tasks. - Integrate Airflow with various machine learning frameworks and tools. - Deploy machine learning models using automated pipelines. - Monitor and optimize machine learning workflows in a production environment.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data scientists and developers who aim to apply machine learning algorithms efficiently using the Google Colab environment. By the end of this training, participants will be able to: - Set up and navigate Google Colab for government machine learning projects. - Understand and apply various machine learning algorithms. - Utilize libraries such as Scikit-learn to analyze and predict data. - Implement both supervised and unsupervised learning models. - Optimize and evaluate machine learning models effectively.

TinyML is the practice of deploying machine learning models on highly resource-constrained hardware. This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices. Upon completing this training, participants will be able to: - Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy. - Benchmark TinyML models for latency, memory consumption, and energy efficiency. - Implement optimized inference pipelines on microcontrollers and edge devices. - Evaluate trade-offs between performance, accuracy, and hardware constraints. **Format of the Course** - Instructor-led presentations supported by technical demonstrations. - Practical optimization exercises and comparative performance testing. - Hands-on implementation of TinyML pipelines in a controlled lab environment. **Course Customization Options for Government** For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training (online or onsite) is designed for government professionals at an advanced level who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems for government. By the end of this training, participants will be able to: - Understand the challenges of explainability in deep learning. - Implement advanced XAI techniques for neural networks. - Interpret decisions made by deep learning models. - Evaluate the trade-offs between performance and transparency.

This instructor-led, live training (conducted either online or on-site) is designed for government professionals at the beginner level who wish to understand and apply artificial intelligence (AI) technologies within the semiconductor manufacturing industry. By the end of this training, participants will be able to: - Comprehend the fundamental principles of AI and their application in semiconductor manufacturing. - Identify areas within semiconductor manufacturing where AI can be effectively deployed. - Utilize AI tools and techniques to enhance production efficiency and quality control. - Implement basic AI models to optimize manufacturing processes for government use.

Docker is a containerization platform designed to create reproducible, portable, and scalable environments for machine learning (ML) systems. This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced technical professionals who aim to containerize and operationalize comprehensive ML pipelines using Docker. Upon completion of this training, participants will be able to: - Containerize ML training, validation, and inference workloads. - Design and orchestrate end-to-end ML pipelines using Docker and complementary tools. - Implement versioning, reproducibility, and continuous integration/continuous deployment (CI/CD) for ML components. - Deploy, monitor, and scale ML services in containerized environments. **Format of the Course** - Interactive lectures supported by practical demonstrations. - Hands-on exercises focused on constructing real ML pipeline components. - Live-lab implementation for end-to-end containerized workflows. **Course Customization Options** - For customized training aligned with specific ML infrastructure needs, please contact us to discuss options tailored for government and other public sector entities.

This instructor-led, live training in [location] (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically generate projections from executed data analysis for government and enterprise applications. By the end of this training, participants will be able to: - Install ML.NET and integrate it into their application development environment. - Understand the machine learning principles behind ML.NET tools and algorithms. - Build and train machine learning models to perform predictions with provided data effectively. - Evaluate the performance of a machine learning model using ML.NET metrics. - Optimize the accuracy of existing machine learning models based on the ML.NET framework. - Apply the machine learning concepts of ML.NET to other data science applications for government and enterprise use.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data professionals who seek to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks. By the end of this training, participants will be able to: - Understand the fundamental concepts and types of machine learning. - Apply key algorithms for classification, regression, clustering, and association analysis. - Conduct exploratory data analysis and data preparation using Python. - Utilize neural networks for nonlinear modeling tasks. - Implement predictive analytics for business forecasting, including sales data. - Evaluate and optimize model performance using visual and statistical techniques. This training is tailored to enhance the skills of data professionals in the public sector, ensuring they are well-equipped to address complex challenges and improve decision-making processes for government.

This instructor-led, live training in St. Louis (online or onsite) is designed for 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 generating images from text. - Implement complex models and optimizations to produce high-quality image synthesis. - Optimize performance and scalability for large datasets and sophisticated models. - Tune hyperparameters to enhance model performance and generalization. - Integrate Stable Diffusion with other deep learning frameworks and tools, ensuring alignment with public sector workflows and governance requirements for government.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced cybersecurity professionals who aim to enhance their skills in AI-driven threat detection and incident response for government. By the end of this training, participants will be able to: - Implement advanced AI algorithms for real-time threat detection. - Customize AI models to address specific cybersecurity challenges. - Develop automation workflows for efficient threat response. - Ensure the security of AI-driven tools against adversarial attacks.

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who are interested in deploying machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse for government applications. By the end of this training, participants will be able to: - Understand the fundamentals of TinyML and its benefits for edge AI applications. - Set up a development environment suitable for TinyML projects. - Train, optimize, and deploy AI models on low-power microcontrollers. - Utilize TensorFlow Lite and Edge Impulse to implement practical TinyML applications. - Optimize AI models to meet power efficiency and memory constraints.

This instructor-led, live training in [location] (online or onsite) is designed for beginner-level cybersecurity professionals who wish to learn how to leverage artificial intelligence (AI) for enhanced threat detection and response capabilities. By the end of this training, participants will be able to: - Comprehend AI applications in cybersecurity. - Apply AI algorithms for threat detection. - Automate incident response using AI tools. - Integrate AI into existing cybersecurity infrastructure for government.

The instructor-led, live training session, offered via St. Louis (virtual or in-person), is designed for biological scientists seeking to comprehend the underlying mechanics of AlphaFold and apply its predictive models to support experimental workflows. This program is specifically developed for government professionals involved in biological research.

Upon completion of this training, participants will be equipped to:

• Grasp the fundamental principles governing AlphaFold.
• Articulate the operational mechanisms of the AlphaFold system.
• Accurately interpret AlphaFold predictions and associated analytical results.

This instructor-led, live training (online or onsite) is designed for government developers and data scientists at beginner to intermediate levels who wish to learn the basics of LightGBM and explore advanced techniques. By the end of this training, participants will be able to: - Install and configure LightGBM. - Understand the theory behind gradient boosting and decision tree algorithms. - Use LightGBM for basic and advanced machine learning tasks. - Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation. - Integrate LightGBM with other machine learning frameworks. - Troubleshoot common issues in LightGBM. This training is tailored to enhance the technical capabilities of participants, ensuring they are well-equipped to apply these skills effectively in their roles for government.

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.

This instructor-led, live training (available online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of applications, including those relevant to government operations.

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

• Comprehend the principles of Stable Diffusion and its application in image generation.
• Develop and train Stable Diffusion models for various image generation tasks.
• Utilize Stable Diffusion for diverse image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Enhance the performance and stability of Stable Diffusion models to meet rigorous standards for government use cases.

The objective of this course is to enhance proficiency in applying Machine Learning methods in practical scenarios. Utilizing the Python programming language and its various libraries, and through a wide range of practical examples, this course instructs participants on how to effectively use key Machine Learning components, make informed data modeling decisions, interpret algorithm outputs, and validate results.

Our goal is to equip you with the skills necessary to confidently understand and utilize the essential tools from the Machine Learning toolbox, while avoiding common pitfalls in Data Science applications for government.

This resource provides program developers and data analysts with the essential methodologies required to construct machine learning frameworks from the ground up utilizing Python. It addresses the fundamental concepts governing supervised learning techniques, including classification and regression, as well as unsupervised methods such as clustering and anomaly detection, alongside sophisticated neural network designs. The material details established protocols for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate direct AI development. This content is designed to assist professionals in deploying functional models, assessing algorithmic constraints, and executing applied projects aimed at addressing complex operational challenges for government entities.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, enabling agents to make decisions through interaction with their environments. This technology is pivotal in driving many modern artificial intelligence advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial-and-error learning using a reward system.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and data scientists who wish to gain proficiency in Deep Reinforcement Learning techniques. The goal is to build intelligent agents capable of autonomous decision-making in complex environments, which can be particularly useful for government applications.

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

• Understand the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement key RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world scenarios, including games, robotics, and decision optimization for government use cases.
• Troubleshoot, visualize, and optimize the training performance of DRL models using modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course, such as using PyTorch instead of TensorFlow, please contact us to arrange.

This module examines the foundational principles of artificial intelligence and their impact on digital strategy, automation, and enterprise decision-making. It provides a comprehensive overview of core concepts, including the evolution of AI, problem-solving frameworks, knowledge representation, probabilistic reasoning, and machine learning methodologies, alongside capabilities related to communication, perception, and autonomous operations. Designed for government executives and technical architects, this content enables the evaluation of AI-driven transformation initiatives, the assessment of emerging technology trends, and the implementation of practical intelligent solutions to enhance operational agility.

This course provides an overview of artificial intelligence, with a focus on machine learning and deep learning, as applied to the automotive industry. It is designed to help participants identify which technologies can potentially be utilized in various scenarios within a vehicle, ranging from basic automation to image recognition and autonomous decision-making for government applications.

This 8-day program offers a comprehensive journey from solid Python engineering foundations to advanced AI system design. Participants will develop disciplined coding practices, master statistical and deep learning techniques, and construct production-ready generative AI and agent-based systems. The emphasis is on reliability, evaluation, safety, and real-world deployment, ensuring alignment with the stringent requirements for government and other critical sectors.

Artificial Neural Networks are computational models utilized in the creation of Artificial Intelligence (AI) systems designed to perform tasks that require "intelligent" capabilities. These networks are frequently employed in Machine Learning (ML) applications, which represent one approach to implementing AI. Deep Learning, a specialized subset of ML, further advances these techniques for government and other sectors.

This comprehensive instructional program enhances technical proficiency in data science by examining fundamental machine learning methodologies. The curriculum provides an in-depth analysis of key algorithms, including Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques, supported by theoretical frameworks and practical implementation strategies utilizing authentic datasets. Designed to support workforce development for data analysts, software engineers, and business professionals, this training enables participants to apply effective machine learning solutions. Participants will master essential concepts such as classification performance metrics, cross-validation procedures, bias-variance trade-offs, and deep learning fundamentals to develop robust predictive models for government applications.

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 (available online or onsite) is designed for government data scientists who wish to utilize TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to: - Develop a fraud detection model using Python and TensorFlow. - Construct linear regressions and linear regression models to predict fraudulent activities. - Create an end-to-end AI application for analyzing fraud data for government use.

Machine learning is a branch of Artificial Intelligence that enables computers to learn and improve from experience without being explicitly programmed.

Deep learning is a specialized subfield of machine learning that utilizes methods based on data representation and structures, such as neural networks, to achieve advanced learning capabilities.

Python is a high-level programming language renowned for its clear syntax and code readability, making it an excellent choice for developing complex applications.

This instructor-led, live training provides participants with the skills necessary to implement deep learning models for telecom using Python. Through this course, participants will work 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 and their applications.
• Explore the specific uses and benefits of deep learning in the telecom industry.
• Utilize Python, Keras, and TensorFlow to develop robust deep learning models for telecom.
• Construct a deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options for Government

• To request a customized training tailored to the needs of your government agency, please contact us to arrange.

This practical, instructor-led training serves as a natural continuation of the Python for Data Analysis course.

It introduces participants to fundamental Machine Learning concepts and demonstrates how these can be applied directly to data analysis tasks such as prediction, classification, and segmentation.

The emphasis is on practical application of Machine Learning using familiar tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced mathematical background. This training is tailored for government professionals seeking to enhance their data analysis capabilities.

This course is intended for individuals who already possess a background in data science and statistics. The content is structured to either refresh the knowledge of those familiar with the concepts or provide essential information to those with an appropriate foundation. It is designed to align with the needs of professionals, including those working in government roles, ensuring that the material is relevant and applicable to their specific contexts.

This instructor-led, live training in St. Louis (online or onsite) is designed for engineers who seek to evaluate the available approaches and tools to make an informed decision on adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools suitable for government use.
• Assemble a team with the appropriate skills to construct and support an MLOps system for government operations.
• Prepare, validate, and version data for use by machine learning models in a public sector context.
• Understand the components of an ML Pipeline and the tools necessary to build one that aligns with public sector workflows.
• Experiment with different machine learning frameworks and servers suitable for deployment in government environments.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable, meeting governance and accountability standards for government.

This instructor-led, live training in St. Louis (online or onsite) is designed for intermediate-level data analysts, developers, and aspiring data scientists who seek to apply machine learning techniques using Python to extract insights, make predictions, and automate data-driven decisions for government. By the end of this course, participants will be able to: - Understand and differentiate key machine learning paradigms. - Explore data preprocessing techniques and model evaluation metrics. - Apply machine learning algorithms to solve real-world data problems. - Use Python libraries and Jupyter notebooks for hands-on development. - Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to utilize TensorFlow 2.x for government to build predictors, classifiers, generative models, neural networks, and more. By the end of this training, participants will be able to: - Install and configure TensorFlow 2.x. - Understand the advantages of TensorFlow 2.x over previous versions. - Construct deep learning models. - Implement an advanced image classifier. - Deploy a deep learning model to the cloud, mobile devices, and IoT systems.

This course begins with an introduction to the conceptual knowledge of neural networks and machine learning algorithms, including deep learning (algorithms and applications). Part-1 (40%) of this training focuses on fundamental concepts, which will assist in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2 (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models. Part-3 (40%) is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow. **Audience** This course is designed for engineers who intend to use TensorFlow for their deep learning projects for government. Upon completing this course, participants will: - Have a comprehensive understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). - Understand the structure and deployment mechanisms of TensorFlow. - Be capable of performing installation, production environment setup, architecture tasks, and configuration. - Be able to assess code quality, perform debugging, and monitoring. - Be proficient in implementing advanced production tasks such as training models, building graphs, and logging.