Advanced Python - 1 Day Training Course

This intensive, hands-on course explores advanced Python techniques, engineering best practices, and widely used design patterns to help you build 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 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:

• Leverage 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 short demonstrations.
• Hands-on labs and coding exercises every day.
• A capstone mini-project that integrates patterns, testing, and deployment.

Customization Options

• To request customized training or focus on specific areas (data, web, or infrastructure), please contact us to arrange.

This course imparts practical engineering methods for designing, constructing, evaluating, and deploying autonomous (agentic) systems using Python. Key topics include the agent loop, tool integration, memory and state management, orchestration patterns, safety controls, and production-ready considerations.

Delivered as an instructor-led, live training session (available online or onsite), this programme is designed for intermediate to advanced ML engineers, AI developers, and software engineers who aim to build robust, production-grade 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 enhance agent functionality.
• Implementing both short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and enabling agent composability.
• Applying best practices for safety, access control, and observability in deployed agents.

Course Format

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

Customization Options

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

This course explores practical methodologies for Data Science and AI through Python, empowering professionals with the expertise to analyze data, construct machine learning models, and deploy AI-powered solutions within business environments. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data analytics with Spark, and data-driven storytelling. It is ideal for beginners pursuing a Python data science certification and career-focused 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 training, available both online and onsite, is designed for intermediate-level Python programmers who aim 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.
• Extensive 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 Sweden (online or on-site) is designed for business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

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

This course empowers developers and data analysts with the essential techniques required 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 methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program supports professionals in deploying effective ML models, assessing algorithm constraints, and delivering applied projects tailored to real-world challenges.

This instructor-led, live training in Sweden (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.

This instructor-led, live training in Sweden (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Python to build blockchain and cryptocurrency applications.

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

• Use Python to create and manipulate blockchain and cryptocurrency systems and data.
• Use Python libraries and frameworks such as Flask, PyCrypto, and web3.py to interact with blockchain and cryptocurrency networks and services.
• Use Python to implement smart contracts, consensus algorithms, and cryptographic protocols for blockchain and cryptocurrency applications.
• Use Python to develop and deploy decentralized applications (DApps) that run on blockchain and cryptocurrency platforms.
• Use Python to perform data analysis and visualization on blockchain and cryptocurrency data.

Course Overview

This practical training program is tailored for data engineering professionals looking to develop concrete skills in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world applications, addressing model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that advance from fundamental concepts to the development of deployable AI workflows.

Training Format

• On-site classroom instruction

• Instructor-led sessions featuring guided practice

• Interactive discussions alongside real-world case studies

• Daily hands-on 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

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

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in Sweden (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 Sweden (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 intend to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

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

• Set up 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 Sweden (online or onsite) is tailored for developers who want to use FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and quickly.

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

• Set up the required development environment for creating APIs with Python and FastAPI.
• Develop APIs faster and with greater ease using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to databases 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 Sweden (online or onsite) is aimed at data scientists who wish 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 aims to equip participants with foundational proficiency in applying Machine Learning techniques in real-world scenarios. By leveraging the Python programming language and its extensive ecosystem of libraries, along with numerous practical examples, the curriculum covers the essential components of Machine Learning. It guides learners on making informed data modeling decisions, interpreting algorithm outputs, and validating results effectively.

Our objective is to empower you with the confidence to utilize the most fundamental tools from the Machine Learning toolkit while helping you sidestep common pitfalls associated with Data Science applications.