Python for Matlab Users 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 annotations, concurrency models, architectural patterns, and deployment-ready workflows.

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

By the end of this training, participants will be able 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 using 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 brief 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.

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

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced ML engineers, AI developers, and software engineers who want to build robust, production-ready autonomous agents using Python.

Upon completing 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 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.

Explore practical methodologies for Data Science and Artificial Intelligence (AI) using Python. This course equips professionals with the essential skills to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Topics covered include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing (NLP), big data processing with Spark, and data-driven storytelling. It is ideal for beginners looking to obtain a Python data science certification and acquire career-ready analytics training.

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

This instructor-led live training, available both online and 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 Nigeria (online or onsite) is designed for business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

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

This course empowers developers and data analysts with the essential skills to construct machine learning solutions from the ground up using Python. It explores the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning techniques like clustering and anomaly detection, alongside advanced neural network structures. Participants will learn proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program guides professionals in deploying functional ML models, assessing algorithmic constraints, and delivering applied projects that address real-world challenges.

This instructor-led, live training in Nigeria (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 crafted for professionals with a foundation in data engineering who wish to develop competent skills in artificial intelligence, Python, and large language models. The programme emphasizes real-world applications, addressing model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that transition from fundamental concepts to the construction of deployable AI workflows.

Training Format

• Face-to-face classroom instruction

• Instructor-led sessions accompanied by guided practice

• Interactive discussions and analysis of real-world case studies

• Daily practical 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 functioning of large language models and how to leverage them effectively

• Design and refine prompts to ensure reliable outputs

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

• Integrate AI capabilities into data engineering pipelines

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

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

• Configure the environment to start developing big data processing solutions 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 Nigeria (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 (available online or onsite) is tailored for developers who want 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 Nigeria (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 Nigeria (online or onsite) is tailored for data scientists who want to utilise TensorFlow for analysing potential fraud data.

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

• Create a fraud detection model using Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This course is designed to equip you with the foundational skills needed to effectively apply Machine Learning techniques in real-world scenarios. By leveraging Python and its extensive ecosystem of libraries, alongside numerous hands-on examples, participants will learn to master the core components of Machine Learning. You will gain the ability to make informed decisions about data modelling, interpret algorithm outputs, and rigorously validate results.

Our objective is to empower you with the confidence to utilise essential tools from the Machine Learning repertoire while helping you steer clear of common pitfalls often encountered in Data Science projects.

This course is designed to equip participants with practical proficiency in applying Machine Learning techniques. Using the Python programming language alongside its extensive ecosystem of libraries, and drawing on numerous real-world examples, the curriculum covers the essential components of Machine Learning. Learners will be guided on how to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

The primary objective is to empower you with the confidence to utilise fundamental Machine Learning tools and to help you steer clear of common pitfalls associated with Data Science applications.