Advanced Machine Learning with Python Training Course

This course provides an in-depth exploration of AI, with a specific focus on Machine Learning and Deep Learning, within the Automotive Industry. It is designed to help participants identify which technologies can be effectively applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making processes.

This program is designed for managers, solutions architects, innovation specialists, CTOs, software architects, and anyone seeking a comprehensive understanding of applied artificial intelligence and its upcoming trends.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development.

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

• Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
• Write and understand procedural, functional, and object-oriented programming code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rules engines and expert systems for problem-solving.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Grasp the fundamental principles of AlphaFold.
• Learn how AlphaFold operates.
• Master the interpretation of AlphaFold predictions and results.

Artificial Neural Networks (ANNs) are computational models utilized in the creation of Artificial Intelligence (AI) systems that can execute complex, intelligent tasks. These networks are a core component of Machine Learning (ML) applications, which represent one of the primary implementations of AI. Deep Learning is specifically a specialized subset of Machine Learning.

This is a 4-day course introducing AI and its application. There is an option to have an additional day to undertake an AI project on completion of this course.

This is a 4-day course introducing AI and its application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.

This instructor-led, live training in Bulgaria (online or onsite) is designed for intermediate-level data scientists and statisticians who aim to prepare data, build models, and effectively apply machine learning techniques in their professional domains.

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

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems that can execute 'intelligent' tasks. Neural Networks are frequently applied within Machine Learning (ML) contexts, which constitute one form of AI implementation. Deep Learning represents a specialized subset of ML.

This instructor-led, live training in Bulgaria (available online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, while 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 start 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 Bulgaria (online or onsite) offers 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.

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

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions through interaction with their environments. This approach drives many modern AI innovations, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions via trial and error using reward-based learning.

This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists who want to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

Upon completing this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end 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 training is designed for individuals seeking to understand the core principles of neural networks and their practical applications.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.