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 emphasizes modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

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

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

• Leverage 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 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 container technologies.

Course Format

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

Customization Options

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

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

Delivered as an instructor-led live training (available online or onsite), this program is designed for intermediate to advanced ML engineers, AI developers, and software engineers aiming to construct 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 expand agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for building agents using 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.

This course explores practical methodologies for Data Science and AI using Python, equipping professionals with the expertise to analyze data, develop machine learning models, and deploy AI-powered applications within business environments. It covers the CRISP-DM framework, 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 looking to obtain a Python data science certification and receive career-focused 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 developers 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 course on Applied AI from Scratch in Python provides programmers and data analysts with the essential skills needed to develop machine learning solutions entirely from the ground up using Python. It covers the fundamental concepts of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside an exploration of advanced neural network architectures. Participants will examine effective techniques for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical machine learning models, assessing the constraints of various algorithms, and completing applied projects designed to solve real-world challenges.

This instructor-led, live training in Italy (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.

Course Overview

This practical training is tailored for professionals with a data engineering background who aim to develop tangible skills in artificial intelligence, Python, and large language models (LLMs). The curriculum emphasizes real-world applications, addressing model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that advance from foundational concepts to the construction of deployable AI workflows.

Training Format

• Classroom-based in-person training

• Instructor-led sessions featuring guided practice

• Interactive discussions and analysis of real-world case studies

• Daily hands-on exercises

Course Objectives

• Grasp core AI and machine learning concepts pertinent to contemporary applications

• Enhance Python capabilities for AI development and data workflows

• Comprehend the mechanics of large language models and utilize them effectively

• Design and refine prompts to ensure reliable outputs

• Develop end-to-end AI solutions leveraging APIs and frameworks

• Integrate AI functionalities into data engineering pipelines

This instructor-led, live training in Italy (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 Italy (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 Italy (online or onsite) is aimed at 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 tailored for developers aiming to utilize the FARM (FastAPI, React, and MongoDB) stack to construct dynamic, high-performance, and scalable web applications.

By the conclusion of this training, participants will be capable of:

• Setting up the required development environment that integrates FastAPI, React, and MongoDB.
• Understanding the key concepts, features, and benefits of the FARM stack.
• Learning how to build REST APIs with FastAPI.
• Learning how to design interactive applications with React.
• Developing, testing, and deploying applications (front end and back end) using the FARM stack.

This instructor-led live training, conducted Italy (online or onsite), is designed for developers aiming to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater speed and ease.

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

• Set up the necessary development environment to create APIs using Python and FastAPI.
• Develop APIs more quickly and simply using the FastAPI library.
• Understand how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication features in APIs using FastAPI tools.
• Create container images and deploy web APIs to cloud servers.

This course aims to develop practical proficiency in applying Machine Learning methods. Leveraging Python and its extensive libraries, along with numerous hands-on examples, participants will learn to utilize the core components of Machine Learning. The curriculum covers making informed data modeling decisions, interpreting algorithm outputs, and validating results.

We strive to equip you with the confidence to master the essential tools in the Machine Learning toolbox, while helping you avoid common pitfalls associated with Data Science applications.

This instructor-led live training in Italy (online or on-site) targets developers and data scientists who intend to use TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and related 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 a conceptual foundation in neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, aiding 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 in TensorFlow.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN.
• comprehend the structure and deployment mechanisms of TensorFlow.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitoring.
• be able to implement advanced production-grade tasks such as model training, graph construction, and logging.