Advanced Machine Learning with Python Training Course

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive sector. It guides participants in identifying suitable technologies for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making systems.

Delving into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course examines essential concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics such as communication, perception, and autonomous action. It empowers executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technological trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Norway (online or on-site) 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 employed in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely utilized in Machine Learning (ML) applications, which serve as a specific implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

The course Applied AI from Scratch in Python empowers programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers core 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 examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess algorithmic limitations, and complete applied projects designed for solving real-world problems.

This instructor-led live training in Norway (online or onsite) is aimed at 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 Norway (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build 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 Norway (online or onsite) 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 reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives many contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions through trial-and-error mechanisms based on reward signals.

This instructor-led live training is available both online and onsite. It is designed for intermediate-level developers and data scientists who want to master and apply Deep Reinforcement Learning techniques to create intelligent agents capable of making autonomous decisions in complex settings.

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

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Implement essential 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 to practical applications like gaming, robotics, and decision optimization.
• Use modern tools to troubleshoot, visualize, and optimize training performance.

Format of the Course

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

Course Customization Options

• To request a customized version of this course (for example, using PyTorch instead of TensorFlow), please contact us to make arrangements.

This live, instructor-led training, conducted in Norway (either online or onsite), is designed for intermediate-level developers, data scientists, and AI professionals seeking to apply TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be capable of:

• Comprehending the basics of TensorFlow Lite and its function in Edge AI.
• Developing and optimizing AI models via TensorFlow Lite.
• Deploying TensorFlow Lite models onto various edge devices.
• Applying tools and methods for model conversion and optimization.
• Implementing functional Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Norway (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 Norway (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 initiates with a conceptual foundation in neural networks, covering broad aspects of machine learning algorithms and deep learning (including algorithms and their applications).

Part-1 (40%) of this training concentrates on fundamentals, aiding you in selecting 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 heavily focused on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will utilize 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
• understand TensorFlow's structure and deployment mechanisms
• be capable of handling installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production tasks such as training models, building graphs, and logging

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals eager to investigate cutting-edge XAI techniques for deep learning models, with a strong emphasis on developing interpretable AI systems.

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

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