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, typing, concurrency models, architectural patterns, and deployment-ready workflows.

This instructor-led live training (available online or on-site) is designed for intermediate to advanced Python developers who want to adopt professional practices and patterns 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.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Write well-tested code with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

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

Course Customization Options

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

This course provides practical engineering techniques for designing, building, 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 production considerations.

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 looking to create robust, production-ready autonomous agents using Python.

Upon completion, participants will be able to:

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to extend 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 involving agent development 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 explores practical methods for Data Science and AI using Python, equipping professionals with the skills to explore data, build machine learning models, and deploy AI-driven applications in business contexts; It covers CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data with Spark, and data-driven storytelling; It is ideal for beginners seeking a Python data science certification and career-ready analytics training.

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

Delivered as an instructor-led live training (available online or onsite), this course is designed for intermediate-level Python programmers who aim to design, implement, and deploy AI solutions.

Upon completing this training, participants will be capable of:

• Implementing AI algorithms using Python’s core AI libraries.
• Working with supervised, unsupervised, and reinforcement learning models.
• Integrating AI solutions into existing applications and workflows.
• Evaluating model performance and optimizing for accuracy and efficiency.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation within a live-lab environment.

Customization Options

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

This instructor-led, live training in Malaysia (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 provides programmers and data analysts with the fundamental techniques needed to construct machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The content enables professionals to implement practical machine learning models, evaluate algorithm limitations, and execute applied projects designed for real-world problem solving.

This instructor-led, live training in Malaysia (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 hands-on expertise in artificial intelligence, Python, and large language models. Emphasizing real-world application, the curriculum covers model utilization, prompt engineering, and the development of AI-driven solutions. Participants will engage in a series of progressive exercises, advancing from foundational concepts to the construction of deployable AI workflows.

Training Format

• In-person classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions complemented by 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 mechanics of large language models and learn to utilize them effectively

• Design and refine prompts to ensure reliable outputs

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

• Seamlessly integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Malaysia (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 Malaysia (online or onsite) is designed for 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 aimed at 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 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 Malaysia (online or on-site) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly.

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 Malaysia (online or on-site) is designed for data scientists who wish to utilise TensorFlow to analyse potential fraud data.

By the conclusion 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 analysing fraud data.

This course is designed to equip participants with fundamental practical skills in applying Machine Learning techniques. Utilizing Python and its extensive ecosystem of libraries, alongside numerous real-world examples, the curriculum covers essential Machine Learning components. Learners will gain the ability to make informed data modeling decisions, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to utilize core Machine Learning tools and to steer clear of common challenges associated with Data Science applications.

This course aims to equip participants with practical proficiency in applying Machine Learning techniques. Leveraging the Python programming language and its extensive array of libraries, alongside numerous hands-on examples, the training covers the essential components of Machine Learning. Attendees will learn how to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

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