Fraud Detection with Python and TensorFlow Training Course

This intensive, hands-on course explores advanced Python techniques, engineering best practices, and widely adopted design patterns to help you build maintainable, testable, and high-performance Python applications. It focuses on modern tooling, type hints, concurrency models, architectural patterns, and deployment-ready workflows.

Delivered as an instructor-led, live training session (available online or onsite), this program is designed for intermediate to advanced Python developers who aim to adopt professional practices and patterns for production-grade Python systems.

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

• Utilize Python typing, dataclasses, and type-checking to enhance code reliability.
• Apply design patterns and architectural principles to structure robust applications.
• Effectively implement concurrency and parallelism using asyncio and multiprocessing.
• Develop well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimize, and secure Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containerization.

Course Format

• Interactive lectures accompanied by concise demonstrations.
• Practical labs and coding exercises each day.
• A capstone mini-project that integrates patterns, testing, and deployment.

Customization Options

• To request customized training or focus on specific areas (such as data, web, or infrastructure), please contact us to arrange.

This course delivers practical engineering methodologies for designing, constructing, testing, and deploying agentic (autonomous) systems using Python. Key topics include the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production-grade considerations.

Delivered as an instructor-led, live training session (available online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers looking to build robust, production-ready autonomous agents in Python.

Upon completing this training, participants will be equipped to:

• Design and implement agent loops and decision-making workflows.
• Integrate external tools and APIs to enhance agent capabilities.
• Implement memory architectures for both short-term and long-term state management.
• Coordinate multi-step orchestrations and ensure agent composability.
• Apply best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on building agents with Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Customization Options

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

This course provides a practical exploration of Data Science and AI utilizing Python, empowering professionals with the capabilities to analyze data, construct machine learning models, and implement AI-powered applications within business environments. It covers CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is an ideal choice for beginners pursuing a Python data science certification and seeking career-ready analytics training.

Artificial Intelligence with Python focuses on building intelligent systems by leveraging Python’s comprehensive ecosystem of AI and machine learning libraries.

This instructor-led live training, available either online or onsite, is designed for intermediate-level Python programmers who aim to design, implement, and deploy AI solutions using Python.

Upon completing this training, participants will be equipped to:

• Develop AI algorithms utilizing Python’s primary AI libraries.
• Apply supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions seamlessly into existing applications and workflows.
• Assess model performance and optimize for both accuracy and efficiency.

Course Format

• Engaging lectures and interactive discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Customization Options

• To arrange a customized training session for this course, please get in touch with us.

This instructor-led, live training in Argentina (online or onsite) is designed for business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

• Utilize algorithms to rapidly buy and sell securities at specialized increments.
• Lower costs associated with trading through the application of algorithmic methods.
• Automatically monitor stock prices and execute trades.

Applied AI from Scratch in Python provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methodologies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program assists professionals in implementing practical ML models, evaluating the limitations of algorithms, and completing applied projects aimed at solving real-world problems.

This instructor-led, live training in Argentina (online or onsite) is aimed at intermediate-level Python developers who wish to enhance their Python development experience using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Understand the AWS Cloud9 IDE interface and features.
• Write, debug, and deploy Python applications in AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployments.

This instructor-led, live training in Argentina (online or onsite) is aimed at advanced-level professionals who wish 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 instructor-led live training in Argentina (available online or onsite) targets data scientists and software engineers who intend to utilize Dask alongside the Python ecosystem to construct, scale, and analyze large datasets.

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

• Configure the environment necessary to begin developing big data solutions using Dask and Python.
• Investigate the features, libraries, tools, and APIs provided by Dask.
• Comprehend how Dask enhances parallel computing capabilities in Python.
• Acquire the skills to scale the Python ecosystem, including NumPy, SciPy, and Pandas, using Dask.
• Optimize the Dask environment to ensure high performance when managing large datasets.

This instructor-led, live training in Argentina (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.

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.
• 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.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level data scientists and developers aiming to comprehend and implement deep learning methods using the Google Colab environment.

Upon completing this training, participants will be capable of:

• Configuring and navigating Google Colab for deep learning initiatives.
• Grasping the core concepts of neural networks.
• Developing deep learning models utilizing TensorFlow.
• Training and assessing deep learning models.
• Leveraging advanced capabilities of TensorFlow for deep learning.

This instructor-led live training, offered online or onsite, targets developers who want to employ the FARM (FastAPI, React, and MongoDB) stack to construct dynamic, high-performance, and scalable web applications.

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

• Set up the required development environment integrating FastAPI, React, and MongoDB.
• Understand the fundamental concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led live training in Argentina (online or onsite) is designed for developers who want to use Python with FastAPI to build, test, and deploy RESTful APIs more easily and quickly.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led live training in Argentina (online or onsite) targets developers and data scientists aiming to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and other related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over previous iterations.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to cloud environments, mobile devices, and IoT systems.

This course provides a conceptual foundation in neural networks, with a broad overview of machine learning algorithms and deep learning techniques, including their algorithms and practical applications.

Part-1 (40%) of the training focuses heavily on fundamentals, helping you select the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) is extensively based on TensorFlow, the API for Google's open-source library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

This course is designed for engineers who wish to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• grasp TensorFlow’s structure and deployment mechanisms
• be capable of handling installation, production environment setup, and architecture configuration tasks
• be able to assess code quality, perform debugging, and implement monitoring
• be proficient in implementing advanced production tasks such as model training, graph construction, and logging