Python for Matlab Users Training Course

This intensive, hands-on course covers advanced Python techniques, engineering best practices, and commonly used design patterns to build maintainable, testable, and high-performance Python applications. It emphasizes modern tooling, typing, concurrency models, architecture patterns, and deployment-ready workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level Python developers who wish 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 increase code reliability.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Build 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 delivers practical engineering methods for designing, constructing, validating, and deploying autonomous (agentic) systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production-level considerations.

Delivered as an instructor-led live session (available online or onsite), this training is tailored for intermediate to advanced ML engineers, AI developers, and software engineers looking to build robust, production-ready autonomous agents in Python.

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

• Designing and implementing agent loops and decision-making workflows.
• Integrating external tools and APIs to expand agent functionality.
• Implementing both short-term and long-term memory structures for agents.
• Coordinating multi-step orchestrations and ensuring agent composability.
• Applying best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on building agents using Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Customization Options

• To arrange customized training for this course, please reach out to us.

This course offers a hands-on exploration of Data Science and AI through Python, empowering professionals with the expertise to analyze data, construct machine learning models, and implement AI-powered applications within business environments. The curriculum spans 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 seeking career-focused analytics training.

The "Artificial Intelligence with Python" course focuses on developing intelligent systems by leveraging Python’s extensive ecosystem of AI and machine learning libraries.

This instructor-led live training, available online or onsite, is designed for intermediate-level Python programmers looking 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 optimize for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises and practice.
• Practical 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 Turkey (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.

This course empowers programmers and data analysts with the essential techniques for constructing 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 methods such as clustering and anomaly detection, alongside advanced neural network architectures. The curriculum reviews established practices for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. It enables professionals to implement effective ML models, assess algorithm constraints, and execute applied projects designed to solve real-world challenges.

This instructor-led, live training in Turkey (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 program is tailored for data engineering professionals aiming to develop hands-on expertise in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world applications, focusing on model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in a series of progressive exercises that transition from foundational concepts to the construction of deployable AI workflows.

Training Format

• In-person classroom instruction

 • Instructor-led sessions featuring guided practice

• Interactive discussions and real-world case studies

 • Daily practical exercises

Course Objectives

• Grasp core AI and machine learning principles applicable to contemporary systems

 • Enhance Python proficiency for AI development and data processing

• Comprehend the mechanics of large language models and effective utilization strategies

• Design and refine prompts to ensure consistent and reliable outputs

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

 • Seamlessly integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Turkey (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 Turkey (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, offered online or onsite, targets developers who want to utilize the FARM stack (FastAPI, React, and MongoDB) to build dynamic, high-performance, and scalable web applications.

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

• Configure the necessary development environment integrating 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 Turkey (online or onsite) is targeted at developers who want to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater speed and ease.

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

• Configure the necessary development environment for developing APIs with Python and FastAPI.
• Create APIs more quickly and easily 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 Turkey (online or onsite) targets data scientists aiming to use TensorFlow for analyzing 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 regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This course aims to equip participants with practical proficiency in applying Machine Learning techniques. Leveraging Python and its extensive ecosystem of libraries, along with numerous hands-on examples, learners will master the essential components of Machine Learning. The curriculum covers making informed data modeling decisions, interpreting algorithmic outputs, and validating results effectively.

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

This instructor-led live training in Turkey (available online or on-site) is designed for network engineers who wish to maintain, manage, and design computer networks using Python.

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

• Optimize and utilize Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
• Master multi-threading and multiprocessing techniques in network automation.
• Employ GNS3 and Python for network programming tasks.