Advanced Python - 1 Day Training Course

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

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced-level Python developers who aim 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.
• Utilize design patterns and architectural principles to structure robust applications.
• Implement concurrency and parallelism effectively 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 short demonstrations.
• Hands-on labs and coding exercises each day.
• Capstone mini-project integrating 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 delivers practical engineering methodologies for designing, building, testing, and deploying autonomous (agentic) systems using Python. The curriculum covers 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 onsite, is designed for intermediate to advanced ML engineers, AI developers, and software engineers aiming to construct robust, production-ready autonomous agents in Python.

Upon completing this training, participants will be able to:

• Design and implement agent loops and decision-making workflows.
• Integrate external tools and APIs to enhance agent functionality.
• Develop short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and enable agent composability.
• Apply 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 request a customized training session for this course, please contact us.

Explores practical methodologies for Data Science and AI leveraging Python. It equips professionals with the capabilities to analyze data, construct machine learning models, and implement AI-driven applications within business settings. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning via TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. This course is ideal for beginners aiming for a Python data science certification and career-ready analytics training.

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

This instructor-led live training, available online or onsite, targets intermediate-level Python developers looking to design, implement, and deploy AI solutions using Python.

Upon completing this training, participants will be able to:

• Implement AI algorithms utilizing 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 Serbia (online or onsite) is designed for business analysts who wish to automate their 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 the costs associated with trading through the use of algorithmic techniques.
• Automatically track stock prices and execute trades.

Practical AI Implementation from Scratch in Python provides developers and data analysts with the essential skills needed to construct machine learning models from the ground up using Python. The course covers the fundamental concepts of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network structures. Participants will explore established methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This training enables professionals to deploy effective ML models, assess algorithm constraints, and execute applied projects designed to address real-world challenges.

This instructor-led live training, conducted Serbia (online or onsite), is designed for intermediate-level Python developers aiming to enhance their development experience with 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 wish to develop hands-on skills in artificial intelligence, Python, and large language models. The program emphasizes real-world applications, including the utilization of models, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that transition from foundational concepts to the development of deployable AI workflows.

Training Format

• In-person classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions and analysis of real-world case studies

• Daily practical exercises

Course Objectives

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

• Enhance Python proficiency for AI development and data workflows

• Gain insight into the operation of large language models and effective utilization strategies

• 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 in Serbia (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:

• Configure the environment to begin developing big data processing solutions with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Gain an understanding of how Dask accelerates parallel computing in Python.
• Learn techniques for scaling the Python ecosystem (including Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to ensure high performance when handling large datasets.

This instructor-led, live training in Serbia (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) targets developers who want to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

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

• Configure the necessary development environment integrating FastAPI, React, and MongoDB.
• Comprehend the key concepts, features, and benefits of the FARM stack.
• Build REST APIs using FastAPI.
• Design interactive applications with React.
• Develop, test, and deploy applications (both front end and back end) using the FARM stack.

This instructor-led live training in Serbia (online or onsite) is intended for developers who want to use 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:

• Set up the required development environment to build APIs with Python and FastAPI.
• Create APIs faster and more 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 FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led live training in Serbia (online or on-site) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in 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 establish a foundational proficiency in applying Machine Learning techniques in real-world scenarios. Leveraging the Python programming language and its extensive ecosystem of libraries, along with numerous practical examples, the curriculum covers essential Machine Learning components. Participants will learn how to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

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

This course is designed to equip participants with practical proficiency in applying Machine Learning techniques. By leveraging the Python programming language and its extensive ecosystem of libraries, along with numerous real-world examples, the curriculum covers essential Machine Learning components. Learners will gain insights into making informed data modeling decisions, interpreting algorithmic outputs, and validating results effectively.

Our objective is to empower you with the confidence to utilize core Machine Learning tools while helping you navigate and avoid common pitfalls associated with Data Science applications.