Fraud Detection with Python and TensorFlow Training Course

This four-day course provides an introduction to artificial intelligence (AI) and its applications for government. Participants have the option to extend their learning by one additional day to work on an AI project upon completing the initial course.

This instructor-led, live training in US (online or onsite) is designed for data scientists and software engineers who wish to utilize 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 necessary environment to begin building big data processing systems using Dask and Python. - Explore the features, libraries, tools, and APIs available in Dask. - Understand how Dask enhances parallel computing capabilities in Python. - Learn techniques for scaling the Python ecosystem (including Numpy, SciPy, and Pandas) using Dask. - Optimize the Dask environment to ensure high performance when handling large datasets.

This instructor-led, live training in US (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 session, 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 code deep learning models for NLP using Python libraries. - Create Python scripts that analyze a large collection of images and extract relevant keywords. - Develop Python code that generates coherent captions based on the identified keywords. This training is designed to enhance technical skills in data processing and machine learning, specifically tailored for government applications.

Audience

This course is designed for deep learning researchers and engineers who are interested in leveraging available tools, primarily open source, to analyze computer images. The course provides practical, working examples to enhance skills and knowledge for government and other public sector applications.

This instructor-led, live training (available online or on-site) is designed for developers who aim 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: - Set up a development environment that seamlessly integrates FastAPI, React, and MongoDB. - Understand the fundamental concepts, features, and benefits of the FARM stack. - Learn how to develop REST APIs using FastAPI. - Gain proficiency in designing interactive applications with React. - Develop, test, and deploy both front-end and back-end components of applications using the FARM stack.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly for government projects. By the end of this training, participants will be able to: - Set up the necessary development environment to create APIs using Python and FastAPI. - Develop APIs more quickly and easily leveraging the FastAPI library. - Understand how to create data models and schemas based on Pydantic and OpenAPI standards. - Integrate APIs with a database using SQLAlchemy. - Implement security and authentication in APIs utilizing FastAPI tools. - Build container images and deploy web APIs to a cloud server for government use.

This instructor-led, live training in US (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. By the end of this training, participants will be able to: - Set up the necessary environment to start developing Pandas workflows at scale with Modin. - Understand the features, architecture, and advantages of Modin for government applications. - Differentiate between Modin, Dask, and Ray in terms of their suitability for various data processing tasks. - Perform Pandas operations more efficiently using Modin. - Implement the entire Pandas API and functions to enhance data analysis capabilities.

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.

In this instructor-led, live training in US (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 the second-generation API of Google's open-source software library for deep learning. This system is designed to support research in machine learning and to streamline the transition from research prototype to production systems. ### Audience This course is intended for engineers seeking to use TensorFlow for their deep learning projects, particularly those working in government agencies or public sector organizations. After completing this course, delegates will: - Understand TensorFlow’s structure and deployment mechanisms - Be able to perform installation, production environment setup, architecture tasks, and configuration - Be able to assess code quality, conduct debugging, and implement monitoring - Be able to execute advanced production tasks such as training models, building graphs, and logging for government applications

This course delves into the application of TensorFlow for image recognition, providing specific examples to enhance understanding. **Audience** This course is designed for engineers who aim to leverage TensorFlow for government purposes in the field of Image Recognition. **Learning Objectives** Upon completing this course, participants will be able to: - Understand TensorFlow’s architecture and deployment processes - Perform installation, production environment setup, and configuration tasks - Evaluate code quality, conduct debugging, and implement monitoring - Execute advanced production tasks such as training models, constructing graphs, and logging for government applications

This instructor-led, live training in [location] (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. By the end of this training, participants will be able to: - Install and configure TensorFlow Extended (TFX) and supporting third-party tools for government use. - Use TFX to create and manage a comprehensive ML production pipeline. - Work with TFX components to conduct modeling, training, serving inference, and managing deployments. - Deploy machine learning features to web applications, mobile applications, IoT devices, and other platforms.

TensorFlow™ is an open-source software library designed for numerical computation using data flow graphs.

SyntaxNet is a neural-network framework for natural language processing that operates within TensorFlow.

Word2Vec is a method used to learn vector representations of words, known as "word embeddings." Word2Vec is particularly efficient in learning these embeddings from raw text and offers two models: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (as detailed in Chapter 3.1 and 3.2 of Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable the generation of 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 within their TensorFlow graphs for government applications.

After completing this course, participants will:

• Understand TensorFlow’s structure and deployment mechanisms
• Be capable of performing installation, production environment setup, architectural tasks, and configuration
• Be able to evaluate code quality, perform debugging, and monitoring
• Be proficient in implementing advanced production tasks such as training models, embedding terms, building graphs, and logging

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.