Advanced Machine Learning with Python Training Course

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios: from basic automation and image recognition to autonomous decision-making.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making across enterprise operations. The course covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led live training, offered in Greece (via online or onsite delivery), is designed for biologists who wish to understand the mechanisms of AlphaFold and apply its models to guide their experimental studies.

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

• Understand the core principles of AlphaFold.
• Learn how AlphaFold operates.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions from the ground up using Python. It explores fundamental concepts of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network designs. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks for practical AI development. The program enables professionals to build effective ML models, assess algorithm constraints, and execute applied projects that address real-world challenges.

This instructor-led live training in Greece (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring the code is easy to debug.

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

• Set up the necessary development environment to begin developing neural network models.
• Define and implement neural network models using comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led live training in Greece (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced vision models using Google Colab.

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

• Construct and train Convolutional Neural Networks (CNNs) using TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

This instructor-led, live training in Greece (online or onsite) provides an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through trial-and-error processes guided by reward mechanisms.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to construct intelligent agents capable of making autonomous decisions within complex environments.

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

• Comprehend the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents leveraging TensorFlow or PyTorch.
• Deploy DRL solutions in practical applications, including gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for instance, substituting TensorFlow with PyTorch), please contact us to arrange.

This instructor-led live training in Greece (online or onsite) is tailored for intermediate-level developers, data scientists, and AI practitioners looking to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led live training in Greece (online or onsite) targets data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Greece (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale them up to operate across multiple GPUs in parallel.

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

• Set up the necessary development environment to begin running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

This course provides a conceptual foundation in neural networks and broadly covers machine learning algorithms, deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) of the training is extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging

This instructor-led, live training in Greece (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.