Fraud Detection with Python and TensorFlow Training Course

This intensive, practical course explores advanced Python techniques, engineering best practices, and widely adopted design patterns to help you build maintainable, testable, and high-performance Python applications. The curriculum highlights modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced Python developers aiming to adopt professional practices and patterns for production-grade Python systems.

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

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

Course Format

• Interactive lectures and concise demonstrations.
• Hands-on labs and coding exercises every day.
• A capstone mini-project that integrates patterns, testing, and deployment.

Course Customization Options

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

This course provides practical engineering techniques for designing, building, testing, and deploying agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (available online or onsite) is aimed at intermediate-level to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to extend agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customization Options

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

This course delves into practical methodologies for Data Science and AI utilizing Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. It encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is an ideal choice for beginners aiming for a Python data science certification and seeking career-ready analytics training.

Artificial Intelligence with Python involves creating intelligent systems by leveraging Python’s comprehensive ecosystem of AI and machine learning libraries.

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

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

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led live training in Brazil (online or onsite) targets business analysts who wish to automate trading using algorithmic trading, Python, and R.

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

• Utilize algorithms to rapidly buy and sell securities at specialized increments.
• Decrease costs associated with trading by leveraging algorithmic trading techniques.
• Automatically monitor stock prices and execute trades.

Practical AI Development from the Ground Up in Python empowers developers and data analysts with essential techniques for constructing machine learning solutions entirely from scratch using Python. The course covers fundamental concepts such as supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and complex neural network architectures. It explores effective strategies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. Participants will learn to deploy functional ML models, assess algorithm constraints, and execute applied projects designed to address real-world challenges.

This instructor-led, live training in Brazil (online or onsite) is designed for intermediate-level Python developers seeking to enhance their Python development experience with AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Navigate the AWS Cloud9 IDE interface and utilize its features.
• Write, debug, and deploy Python applications within AWS Cloud9.
• Collaborate effectively with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployment scenarios.

This instructor-led live training in Brazil (online or onsite) targets advanced professionals seeking 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 Brazil (online or on-site) is designed for data scientists and software engineers who aim to utilize Dask with the Python ecosystem to construct, scale, and analyze large datasets.

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

• Configure the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

This instructor-led, live training in Brazil (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 Brazil (online or on-site) targets intermediate-level data scientists and developers who wish 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 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, offered online or onsite, is designed for developers seeking to utilize the FARM stack (FastAPI, React, and MongoDB) to build dynamic, high-performance, and scalable web applications.

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

• Set up the requisite development environment for FastAPI, React, and MongoDB.
• Understand the fundamental concepts, features, and benefits of the FARM stack.
• Learn to create REST APIs using FastAPI.
• Acquire skills in designing interactive applications with React.
• Develop, test, and deploy applications (both frontend and backend) using the FARM stack.

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

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 Brazil (online or onsite) is tailored for developers and data scientists aiming to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides foundational conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including both algorithms and their practical applications).

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

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

Part 3 (40% of the training) is extensively centered on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will be conducted within TensorFlow.

Audience

This course is designed for engineers who intend to utilize TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the ability to handle installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring
• implement advanced production-level tasks, such as training models, building graphs, and logging