Fraud Detection with Python and TensorFlow Training Course

This intensive, practical course explores advanced Python techniques, engineering best practices, and widely-used 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 instructor-led live training (online or onsite), this program is designed for intermediate to advanced Python developers who want 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.
• Utilise design patterns and architectural principles to structure robust applications.
• Implement concurrency and parallelism effectively using asyncio and multiprocessing.
• Write well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimise, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

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

Course Customization Options

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

This programme imparts practical engineering methodologies for designing, building, testing, and deploying agentic (autonomous) systems using Python. It encompasses 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 targeted at intermediate to advanced ML engineers, AI developers, and software engineers who wish to construct robust, production-ready autonomous agents using Python.

Upon completion of this training, participants will be capable of:

• Designing and implementing the agent loop and decision-making workflows.
• Integrating external tools and APIs to extend agent capabilities.
• Implementing short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and agent composability.
• Applying safety, access control, and observability best practices for deployed agents.

Course Format

• 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 programme offers a hands-on exploration of Data Science and Artificial Intelligence through Python, empowering professionals with the capabilities to analyse data, develop machine learning models, and implement AI-driven solutions within commercial settings. The curriculum encompasses CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning techniques, deep learning using TensorFlow, natural language processing, big data analytics via Spark, and data-driven storytelling. It is particularly suited for novices pursuing Python data science certification and practical analytics training to enhance career prospects.

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

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

Upon completion 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 optimise for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Ample 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 Practical AI Development from the Ground Up in Python empowers programmers and data analysts with essential techniques for constructing machine learning solutions entirely from scratch using Python. It explores the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will examine established techniques for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The programme assists professionals in implementing practical machine learning models, assessing algorithmic constraints, and completing applied projects designed to solve real-world problems.

This instructor-led, live training in Botswana (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 programme is tailored for data engineering professionals aiming to develop tangible skills in artificial intelligence, Python programming, and large language models. The curriculum centres on real-world applications, addressing model utilisation, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that transition from fundamental concepts to the development of deployable AI workflows.

Training Format

• In-person classroom-based training

• Instructor-led sessions featuring guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

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

• Enhance Python proficiency for AI development and data workflows

• Comprehend the mechanics of large language models and their effective utilisation

• Design and optimise prompts for consistent and reliable outputs

• Construct end-to-end AI solutions utilising APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Botswana (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 Botswana (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 Botswana (online or onsite) targets intermediate data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

• Configure and navigate Google Colab for deep learning projects.
• Grasp the fundamental concepts of neural networks.
• Develop deep learning models using TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning.

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

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

• Set up the necessary development environment that integrates 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 Botswana (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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 course is designed to equip participants with practical proficiency in implementing Machine Learning methods. Leveraging the Python programming language and its extensive ecosystem of libraries, alongside numerous real-world examples, the curriculum demonstrates how to utilise the core components of Machine Learning. Attendees will learn to make informed data modelling decisions, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to apply fundamental Machine Learning tools while helping you steer clear of the common pitfalls associated with Data Science applications.

This instructor-led live training in Botswana (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 you with a conceptual understanding of neural networks and machine learning algorithms in general, with a specific focus on deep learning (algorithms and applications).

Part-1 (40%) of this training focuses primarily on fundamentals but will assist you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, and so on.

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 extensively based on TensorFlow, the API of 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 wish to apply TensorFlow to their Deep Learning projects.

Upon completing this course, delegates will:

• possess a strong understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of carrying out installation, production environment, and architecture tasks as well as configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging