Fraud Detection with Python and TensorFlow Training Course

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

Offered as instructor-led live training (available online or on-site), this program is tailored 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 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 with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and secure Python applications for production environments.
• Package, distribute, and deploy Python projects using contemporary tools and containers.

Course Format

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

Course Customization Options

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

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

Delivered as an instructor-led, live training (available online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers seeking 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 along with 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 safety, access control, and observability best practices for 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.

Course Customization Options

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

This course provides a comprehensive exploration of practical Data Science and Artificial Intelligence techniques using Python. It empowers professionals with the essential skills to investigate data, construct machine learning models, and implement AI-driven solutions within business environments. The curriculum covers CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideally suited for beginners pursuing a Python data science certification and those seeking career-ready analytics training.

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

Delivered through an instructor-led live training format, available either online or onsite, this program is designed for intermediate Python developers who aim to design, implement, and deploy AI solutions.

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

• Develop AI algorithms utilizing Python’s core AI libraries.
• Utilize supervised, unsupervised, and reinforcement learning models.
• Seamlessly integrate AI solutions into current applications and workflows.
• Assess model performance and optimize them for both accuracy and efficiency.

Course Format

• Engaging lectures and interactive discussions.
• Ample opportunities for exercises and practical application.
• Hands-on implementation within a live-lab environment.

Customization Options

• For those interested in a customized training version of this course, please get in touch with us to make arrangements.

This instructor-led, live training in Kenya (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.

Applied AI from Scratch in Python empowers software developers and data analysts with essential techniques for constructing machine learning solutions from the ground up using Python. The course covers fundamental concepts such as supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network structures. It explores effective strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to deploy functional ML models, assess algorithm constraints, and execute applied projects aimed at solving real-world challenges.

This instructor-led, live training in Kenya (online or onsite) targets intermediate Python developers seeking to improve their development experience with AWS Cloud9.

By the conclusion 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 delivered Kenya (online or onsite) is designed for advanced-level professionals aiming to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the conclusion 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 Kenya (online or onsite) is tailored for 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 Kenya (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 Kenya (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, available online or onsite, targets developers who wish to utilize 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 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 Kenya (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 instructor-led live training in Kenya (online or onsite) targets developers and data scientists who intend to utilize TensorFlow 2.x to develop predictors, classifiers, generative models, neural networks, and other applications.

By the end of this course, 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 knowledge on neural networks, machine learning algorithms, and deep learning (including algorithms and their applications).

Part-1 (40%) of this training focuses heavily on fundamentals while helping you select 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. The examples and hands-on exercises will all be conducted in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN
• comprehend TensorFlow's structure and deployment mechanisms
• be capable of executing installation / production environment / architecture tasks and configuration
• be able to evaluate code quality, perform debugging, and monitoring
• be able to implement advanced production-like tasks such as training models, building graphs, and logging