Advanced Machine Learning with Python Training Course

This course provides a comprehensive overview of AI, with a focus on Machine Learning and Deep Learning, applied within the automotive industry. It helps participants identify which technologies can be (potentially) utilized across various scenarios in vehicles: ranging from basic automation and image recognition to autonomous decision-making.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, along with aspects of communication, perception, and autonomous action. It guides executives and architects in evaluating AI-driven transformation opportunities, assessing emerging technology trends, and integrating practical intelligent solutions to enhance business agility.

This instructor-led, live training in Turkey (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:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• 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 execute "intelligent" tasks. Neural Networks are frequently applied within Machine Learning (ML) environments, which serve as one of the primary implementations of AI. Deep Learning represents a specialized subset of Machine Learning.

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 targeted at researchers and developers who wish to utilize 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 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 Turkey (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Turkey (online or on-site) provides an introduction into the field of pattern recognition and machine learning. It touches on 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 the principles of reinforcement learning with deep learning architectures, empowering agents to make intelligent decisions through interaction with their surroundings. This technology is foundational to numerous modern AI breakthroughs, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. By leveraging reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and execute autonomous decisions via a trial-and-error process.

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. The goal is to equip participants with the skills to build intelligent agents capable of autonomous decision-making within complex environments.

Upon completing this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL techniques to practical domains like gaming, robotics, and decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementation projects.
• Live coding demonstrations and application-focused projects.

Customization Options

• For customized course variations (such as switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

This instructor-led, live training in Turkey (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners who intend to leverage TensorFlow Lite for Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of TensorFlow Lite and its function in Edge AI.
• Create and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimization.
• Develop practical Edge AI applications using TensorFlow Lite.

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

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

• Set up the necessary development environment to start 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 comprehensive conceptual foundation in neural networks, machine learning algorithms, and deep learning (including algorithms and their practical applications).

Part 1 (40%) of the training focuses on fundamentals, assisting you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

Part 3 (40%) is extensively dedicated to TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers looking to implement TensorFlow in their Deep Learning projects.

Upon completing this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• comprehend TensorFlow's architecture and deployment mechanisms
• be capable of handling installation, production environment setup, architecture design, and configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production tasks, including model training, graph construction, and logging

Designed for advanced professionals seeking to apply state-of-the-art XAI techniques to deep learning models and build interpretable AI systems, this instructor-led live training is available both online and onsite.

Upon completing this training, participants will be able to:

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Assess the trade-offs between model performance and transparency.