Fraud Detection with Python and TensorFlow Training Course

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

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

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

• Apply Python typing, dataclasses, and type-checking to enhance code reliability.
• Utilize design patterns and architecture principles to structure robust applications.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Create 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 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 on specific areas (data, web, or infrastructure), please contact us to make arrangements.

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

This instructor-led live training, available online or on-site, targets intermediate to advanced ML engineers, AI developers, and software engineers looking to build robust, production-ready autonomous agents with Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to enhance 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.

Course Format

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

Course Customization Options

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

Explores practical methods for applying Data Science and AI with Python, equipping professionals with the expertise to analyze data, develop machine learning models, and implement AI-powered applications in business environments. The course covers 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 ideal for beginners seeking a Python data science certification and career-focused analytics training.

Artificial Intelligence with Python involves creating intelligent systems by leveraging Python's robust 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 want 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 Mexico (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

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

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

The Applied AI from Scratch in Python course provides programmers and data analysts with the fundamental skills required to build machine learning solutions from the ground up using Python. The curriculum covers core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This course empowers professionals to implement practical ML models, assess algorithmic limitations, and execute applied projects designed for real-world problem solving.

This instructor-led, live training in Mexico (available online or onsite) is designed for intermediate-level Python developers who want to enhance their 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.
• Navigate the AWS Cloud9 IDE interface and utilize its key features.
• Write, debug, and deploy Python applications within AWS Cloud9.
• Collaborate effectively with other developers via the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services to facilitate advanced deployments.

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

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

• Set up 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 Mexico (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 Mexico (online or onsite) is designed for intermediate-level data scientists and developers aiming to understand and implement deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be capable of:

• Setting up and navigating Google Colab for deep learning initiatives.
• Grasping the core concepts of neural networks.
• Developing deep learning models using TensorFlow.
• Training and assessing deep learning models.
• Leveraging advanced TensorFlow features for deep learning.

This instructor-led live training, offered online or on-site, targets developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the required development environment integrating FastAPI, React, and MongoDB.
• Understand the key 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 Mexico (online or in-person) is designed for developers who want to utilize FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and quickly.

Upon completing this training, participants will be able to:

• Configure the necessary development environment for building APIs with Python and FastAPI.
• Develop APIs more quickly and easily using the FastAPI library.
• Understand how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to databases using SQLAlchemy.
• Implement security and authentication features in APIs using FastAPI tools.
• Create container images and deploy web APIs to cloud servers.

This instructor-led, live training in Mexico (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

By the end 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 starts by providing conceptual knowledge of neural networks, as well as a broader understanding of machine learning algorithms and deep learning (including both algorithms and their applications).

Part-1 (40%) of this training focuses primarily on fundamentals, yet it 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 creation of deep learning models.

Part-3 (40%) of the training is heavily centered on TensorFlow, the API for 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 utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs.
• comprehend TensorFlow’s structure and deployment mechanisms.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitor performance.
• be able to implement advanced production-level tasks such as training models, building graphs, and logging.