Fraud Detection with Python and TensorFlow Training Course

This four-day course provides an introduction to artificial intelligence (AI) and its applications for government. An optional fifth day is available for participants to engage in an AI project following the conclusion of the course.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists and software engineers who wish to use Dask within the Python ecosystem to build, scale, and analyze large datasets for government.

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

• Set up the environment to begin building big data processing capabilities with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask to support their work for government.
• Understand how Dask enhances parallel computing within Python, optimizing performance for large-scale data analysis.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask to meet the demands of complex datasets in the public sector.
• Optimize the Dask environment to ensure high performance and efficiency when handling large datasets for government projects.

This instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy for government applications.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy for government workflows.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code to ensure compliance with public sector governance standards.

In this instructor-led, live training in US Empire, participants will learn to utilize Python libraries for Natural Language Processing (NLP) as they develop an application that processes a set of images and generates descriptive captions.

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

• Design and implement Deep Learning models for NLP using Python libraries.
• Create Python code that analyzes a large collection of images and extracts relevant keywords.
• Develop Python code that generates captions based on the identified keywords, enhancing efficiency and accuracy for government applications.

Audience

This course is designed for deep learning researchers and engineers who are interested in leveraging available tools, primarily open source, for the analysis of computer images. This training provides practical examples to enhance skills and capabilities for government applications.

The course includes working examples to facilitate hands-on learning and application.

This instructor-led, live training, available online or on-site, is designed for developers who wish to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications for government use.

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

• Configure a development environment that seamlessly integrates FastAPI, React, and MongoDB for government projects.
• Understand the essential concepts, features, and benefits of the FARM stack in the context of public sector applications.
• Master the skills needed to build REST APIs with FastAPI for government systems.
• Acquire the knowledge to design interactive applications using React for government interfaces.
• Develop, test, and deploy front-end and back-end applications using the FARM stack in alignment with government workflows and governance standards.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers who wish to utilize FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly for government applications.

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

• Set up the necessary development environment to create APIs using Python and FastAPI for government projects.
• Create APIs more quickly and easily by leveraging the FastAPI library.
• Learn how to develop data models and schemas based on Pydantic and OpenAPI standards.
• Integrate APIs with a database using SQLAlchemy for enhanced data management in public sector workflows.
• Implement security and authentication mechanisms in APIs using the built-in tools provided by FastAPI, ensuring compliance with government security protocols.
• Build container images and deploy web APIs to a cloud server, aligning with government IT infrastructure requirements.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis for government applications.

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

• Set up the necessary environment to start developing scalable Pandas workflows using Modin.
• Understand the features, architecture, and benefits of Modin in the context of public sector data analysis.
• Compare Modin with other parallel computing frameworks such as Dask and Ray.
• Enhance the performance of Pandas operations using Modin.
• Implement the full Pandas API and functions for efficient data processing.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers and data scientists who wish to utilize TensorFlow 2.x for government applications such as building 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 for government use.
• Understand the benefits of TensorFlow 2.x over previous versions in a public sector context.
• Build deep learning models for government projects.
• Implement an advanced image classifier for government applications.
• Deploy a deep learning model to the cloud, mobile, and IoT devices for government use.

In this instructor-led, live training in US Empire (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage machine learning models in a production environment for government.

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

• Train, export, and serve various TensorFlow models.
• Test and deploy algorithms using a unified architecture and set of APIs.
• Extend TensorFlow Serving to support other types of models beyond those developed with TensorFlow.

TensorFlow is a second-generation API of Google's open-source software library for deep learning. The system is designed to facilitate research in machine learning and to enable a smooth transition from research prototype to production systems.

Audience

This course is intended for engineers seeking to use TensorFlow for their deep learning projects for government.

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, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging

This course explores, with specific examples, the application of TensorFlow to the purposes of image recognition for government.

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition in a government context.

After completing this course, delegates will be able to:

• understand TensorFlow’s structure and deployment mechanisms for government applications
• carry out installation, production environment, architecture tasks, and configuration for government systems
• assess code quality, perform debugging, and monitoring for government projects
• implement advanced production tasks such as training models, building graphs, and logging for government operations

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists who wish to transition from training a single machine learning (ML) model to deploying multiple ML models into production environments for government.

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

• Install and configure TFX along with supporting third-party tools.
• Utilize TFX to create and manage a comprehensive ML production pipeline for government.
• Work with TFX components to perform modeling, training, serving inference, and managing deployments in public sector workflows.
• Deploy machine learning capabilities to web applications, mobile applications, IoT devices, and other platforms relevant to government operations.

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, known as "word embeddings." Word2vec is a computationally-efficient predictive model for learning word embeddings from raw text. It comes in two variants: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable users to generate learned embedding models from natural language input.

Audience

This course is designed for developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs for government applications.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to perform installation, production environment configuration, architecture tasks, and system setup
• be capable of assessing code quality, performing debugging, and monitoring performance
• be able to implement advanced production tasks such as training models, embedding terms, building graphs, and logging data

This course begins by providing conceptual knowledge in neural networks and a broad understanding of machine learning algorithms and deep learning (both algorithms and applications).

Part-1 (40%) of this training focuses on the fundamentals, which will assist you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano, a Python library that simplifies the writing of deep learning models.

Part-3 (40%) of the training is extensively based on TensorFlow, the API of 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.

After completing this course, participants will:

• have a solid understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN)
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, perform debugging, and monitor systems
• be proficient in implementing advanced production tasks such as training models, building graphs, and logging